Subcutaneous outpatient management

ABSTRACT

A method of administering insulin includes receiving subcutaneous information for a patient at a computing device and executing a subcutaneous outpatient program for determining recommended insulin dosages. The subcutaneous outpatient program includes obtaining blood glucose data of the patient from a glucometer in communication with the computing device, aggregating blood glucose measurements to determine a representative aggregate blood glucose measurement associated with at least one scheduled blood glucose time interval, and determining a next recommended insulin dosage for the patient based on the representative aggregate blood glucose measurement and the subcutaneous information. The method also includes transmitting the next recommended insulin dosage to a portable device associated with the patient. The portable device displays the next recommended insulin dosage.

CROSS REFERENCE TO RELATED APPLICATIONS

This U.S. patent application is a continuation of, and claims priorityunder 35 U.S.C. § 120 from, U.S. patent application Ser. No. 15/855,315,filed on Dec. 27, 2017, which is a continuation of U.S. patentapplication Ser. No. 14/922,763, filed on Oct. 26, 2015, which claimspriority under 35 U.S.C. § 119(e) to U.S. Provisional Application62/069,195, filed Oct. 27, 2014. The disclosures of these priorapplications are considered part of the disclosure of this applicationand are hereby incorporated by reference in their entireties.

TECHNICAL FIELD

This disclosure relates to a system for managing insulin administrationor insulin dosing.

BACKGROUND

Managing diabetes requires calculating insulin doses for maintainingblood glucose measurements within desired ranges. Managing diabetesrequires calculating insulin doses for maintaining blood glucosemeasurements within desired ranges. Manual calculation may not beaccurate due to human error, which can lead to patient safety issues.Different institutions use multiple and sometimes conflicting protocolsto manually calculate an insulin dosage. Moreover, the diabeticpopulation includes many young children or elderly persons whom havedifficulty understanding calculations for insulin doses.

SUMMARY

One aspect of the disclosure provides a method. The method includesreceiving subcutaneous information for a patient at data processinghardware and executing, at the data processing hardware, a subcutaneousoutpatient process for determining recommended insulin dosages. Thesubcutaneous outpatient process includes obtaining, at the dataprocessing hardware, blood glucose data of the patient from a glucometerin communication with the computing device. The blood glucose dataincludes blood glucose measurements of the patient, blood glucose timesassociated with a time of each blood glucose measurement, and dosages ofinsulin administered by the patient associated with each blood glucosemeasurement. The subcutaneous outpatient process further includesdetermining associated ones of scheduled blood glucose time intervalsfor each of the blood glucose measurements using the data processinghardware based on the blood glucose times and aggregating, using thedata processing hardware, the blood glucose measurements associated withat least one of the scheduled blood glucose time intervals to determinea representative aggregate blood glucose measurement associated with theat least one scheduled blood glucose time interval. The method furtherincludes determining a next recommended insulin dosage for the patientusing the data processing hardware based on the representative aggregateblood glucose measurement and the subcutaneous information andtransmitting the next recommended insulin dosage to a portable deviceassociated with the patient, the portable device displaying the nextrecommended insulin dosage.

Implementations of the disclosure may include one or more of thefollowing optional features. In some implementations, the methodincludes transmitting the subcutaneous outpatient process to anadministration device in communication with the data processinghardware. The administration device includes a doser and anadministration computing device in communication with the doser. Theadministration computing device, when executing the subcutaneousoutpatient program, causes the doser to administer insulin specified bythe subcutaneous outpatient program. The data processing hardware mayobtain the blood glucose data by one or more of the following ways:receiving the blood glucose data from a remote computing device incommunication with the data processing hardware during a batch downloadprocess, the remote computing device executing a download program fordownloading the blood glucose data from the glucometer; receiving theblood glucose data from the glucometer upon measuring the blood glucosemeasurement; receiving the blood glucose data from a meter manufacturercomputing device in communication with the data processing hardwareduring a batch download process, the meter manufacturer receiving theblood glucose data from the glucometer; or receiving the blood glucosedata from a patient device in communication with the data processinghardware and the glucometer, the patient device receiving the bloodglucose data from the glucometer.

In some examples, the method includes aggregating, using the dataprocessing hardware, one or more of the blood glucose measurementsassociated with a breakfast blood glucose time interval to determine arepresentative aggregate breakfast blood glucose measurement andaggregating, using the data processing hardware, one or more of theblood glucose measurements associated with a midsleep blood glucose timeinterval to determine a representative aggregate midsleep blood glucosemeasurement. The method may further include selecting, using the dataprocessing hardware, a governing blood glucose as a lesser one of therepresentative aggregate midsleep blood glucose measurement or therepresentative aggregate breakfast blood glucose measurement anddetermining, using the data processing hardware, an adjustment factorfor adjusting a next recommended basal dosage based on the selectedgoverning blood glucose measurement. The method may further includeobtaining, using the data processing hardware, a previous day'srecommended basal dosage and determining, using the data processinghardware, the next recommended basal dosage by multiplying theadjustment factor times the previous day's recommended basal dosage.

In some implementations, the method includes aggregating, using the dataprocessing hardware, one or more of the blood glucose measurementsassociated with a lunch blood glucose time interval to determine arepresentative aggregate lunch blood glucose measurement and selecting,using the data processing hardware, a governing blood glucose as therepresentative aggregate lunch blood glucose measurement. The method mayalso include determining, using the data processing hardware, anadjustment factor for adjusting a next recommended breakfast bolus basedon the selected governing blood glucose measurement, obtaining, usingthe data processing hardware, a previous day's recommended breakfastbolus, and determining, using the data processing hardware, the nextrecommended breakfast bolus by multiplying the adjustment factor timesthe previous day's recommended breakfast bolus.

In some examples, the method includes aggregating, using the dataprocessing hardware, one or more of the blood glucose measurementsassociated with a dinner blood glucose time interval to determine arepresentative aggregate dinner blood glucose measurement and selecting,using the data processing hardware, a governing blood glucose as therepresentative aggregate dinner blood glucose measurement. The methodmay also include determining, using the data processing hardware, anadjustment factor for adjusting a next recommended lunch bolus based onthe selected governing blood glucose measurement, obtaining, using thedata processing hardware, a previous day's recommended lunch bolus, anddetermining, using the data processing hardware, the next recommendedlunch bolus by multiplying the adjustment factor times the previousday's recommended lunch bolus.

In some implementations, the method includes aggregating, using the dataprocessing hardware, one or more of the blood glucose measurementsassociated with a bedtime blood glucose time interval to determine arepresentative aggregate bedtime blood glucose measurement andselecting, using the data processing hardware, a governing blood glucoseas the representative aggregate bedtime blood glucose measurement. Themethod may also include determining, using the data processing hardware,an adjustment factor for adjusting a next recommended dinner bolus basedon the selected governing blood glucose measurement, obtaining, usingthe data processing hardware, a previous day's recommended dinner bolus,and determining, using the data processing hardware, the nextrecommended dinner bolus by multiplying the adjustment factor times theprevious day's recommended dinner bolus.

In some examples, the method includes aggregating, using the dataprocessing hardware, one or more of the blood glucose measurementsassociated with a selected time interval to determine a representativeaggregate blood glucose measurement associated with the selected timeinterval and selecting, using the data processing hardware, a governingblood glucose as the representative aggregate blood glucose measurementassociated with the selected time interval. The method may furtherinclude determining, using the data processing hardware, an adjustmentfactor for adjusting a next recommended carbohydrate-to-insulin ratiogoverned by the selected time interval based on the selected governingblood glucose measurement, obtaining, using the data processinghardware, a previous day's recommended carbohydrate-to-insulin ratiogoverned by the selected time interval, and determining, using the dataprocessing hardware, the next recommended carbohydrate-to-insulin ratioby multiplying the adjustment factor times the previous day'srecommended carbohydrate-to-insulin ratio. The selected time intervalmay include one of lunch blood glucose interval, a dinner blood glucosetime interval, or a bedtime blood glucose time interval. Each scheduledblood glucose time interval may correlate to an associated blood glucosetype including one of a pre-breakfast blood glucose measurement, apre-lunch blood glucose measurement, a pre-dinner blood glucosemeasurement, a bedtime blood glucose measurement and a midsleep bloodglucose measurement.

In some examples, the method includes determining, using the dataprocessing hardware, the blood glucose type for each of blood glucosemeasurement, the blood glucose type is tagged by the patient whenmeasuring the blood glucose measurement. A portion of the scheduledblood glucose time intervals are associated with time intervals when thepatient is consuming meals and a remaining portion of the scheduledblood glucose time intervals are associated with time intervals when thepatient is not consuming meals.

In some examples, the method includes receiving, at the data processinghardware, a specified date range from a remote healthcare providercomputing device in communication with the data processing hardware andaggregating, using the data processing hardware, one or more of theblood glucose measurements associated with at least one scheduled bloodglucose time intervals and within the specified date range. Therepresentative aggregate blood glucose measurement may include a meanblood glucose value for the associated scheduled blood glucose timeinterval. The representative aggregate blood glucose measurement mayfurther include a median blood glucose value for the associatedscheduled blood glucose time interval.

Another aspect of the disclosure provides a system. The system includesa dosing controller receiving subcutaneous information for a patient andexecuting a subcutaneous outpatient process for determining recommendedinsulin dosages, during subcutaneous outpatient program. The dosingcontroller includes obtaining, at the data processing hardware, bloodglucose data of the patient from a glucometer in communication with thecomputing device, the blood glucose data including blood glucosemeasurements of the patient, blood glucose times associated with a timeof each blood glucose measurement, and dosages of insulin administeredby the patient associated with each blood glucose measurement. Thesystem also includes determining associated ones of scheduled bloodglucose time intervals for each of the blood glucose measurements basedon the blood glucose times and aggregating the blood glucosemeasurements associated with at least one of the scheduled blood glucosetime intervals to determine a representative aggregate blood glucosemeasurement associated with the at least one scheduled blood glucosetime interval. The system also includes determining a next recommendedinsulin dosage for the patient based on the representative aggregateblood glucose measurement and the subcutaneous information andtransmitting the next recommended insulin dosage to a portable deviceassociated with the patient, the portable device displaying the nextrecommended insulin dosage.

This aspect may include one or more of the following optional features.In some implementations, the dosing controller transmits thesubcutaneous outpatient process to an administration device incommunication with the dosing controller. The administration deviceincludes a doser and an administration computing device in communicationwith the doser. The administration computing device, when executing thesubcutaneous outpatient process, causes the doser to administer insulinspecified by the subcutaneous outpatient process. The dosing controllermay obtain the blood glucose data by one or more of the following:receiving the blood glucose data from a remote computing device incommunication with the dosing controller during a batch downloadprocess, the remote computing device executing a download program fordownloading the blood glucose data from the glucometer; receiving theblood glucose data from the glucometer upon measuring the blood glucosemeasurement; receiving the blood glucose data from a meter manufacturercomputing device in communication with the dosing controller during abatch download process, the meter manufacturer receiving the bloodglucose data from the glucometer; or receiving the blood glucose datafrom a patient device in communication with the dosing controller andthe glucometer, the patient device receiving the blood glucose data fromthe glucometer.

The dosing controller may further include aggregating one or more of theblood glucose measurements associated with a breakfast blood glucosetime interval to determine a representative aggregate breakfast bloodglucose measurement and aggregating one or more of the blood glucosemeasurements associated with a midsleep blood glucose time interval todetermine a representative aggregate midsleep blood glucose measurement.The dosing controller may further include selecting a governing bloodglucose as a lesser one of the representative aggregate midsleep bloodglucose measurement or the representative aggregate breakfast bloodglucose measurement, determining an adjustment factor for adjusting anext recommended basal dosage based on the selected governing bloodglucose measurement, obtaining a previous day's recommended basaldosage, and determining the next recommended basal dosage by multiplyingthe adjustment factor times the previous day's recommended basal dosage.

The dosing controller may also include aggregating one or more of theblood glucose measurements associated with a lunch blood glucose timeinterval to determine a representative aggregate lunch blood glucosemeasurement and selecting a governing blood glucose as therepresentative aggregate lunch blood glucose measurement. The dosingcontroller may further include determining an adjustment factor foradjusting a next recommended breakfast bolus based on the selectedgoverning blood glucose measurement, obtaining a previous day'srecommended breakfast bolus, and determining the next recommendedbreakfast bolus by multiplying the adjustment factor times the previousday's recommended breakfast bolus.

In some examples, the dosing controller includes aggregating one or moreof the blood glucose measurements associated with a dinner blood glucosetime interval to determine a representative aggregate dinner bloodglucose measurement and selecting a governing blood glucose as therepresentative aggregate dinner blood glucose measurement. The dosingcontroller may also include determining an adjustment factor foradjusting a next recommended lunch bolus based on the selected governingblood glucose measurement, obtaining a previous day's recommended lunchbolus, and determining the next recommended lunch bolus by multiplyingthe adjustment factor times the previous day's recommended lunch bolus.

In some implementations, the dosing controller includes aggregating oneor more of the blood glucose measurements associated with a bedtimeblood glucose time interval to determine a representative aggregatebedtime blood glucose measurement and selecting a governing bloodglucose as the representative aggregate bedtime blood glucosemeasurement. The dosing controller may also include determining anadjustment factor for adjusting a next recommended dinner bolus based onthe selected governing blood glucose measurement, obtaining a previousday's recommended dinner bolus, and determining the next recommendeddinner bolus by multiplying the adjustment factor times the previousday's recommended dinner bolus.

The dosing controller may further include aggregating one or more of theblood glucose measurements associated with a selected time interval todetermine a representative aggregate blood glucose measurementassociated with the selected time interval and selecting a governingblood glucose as the representative aggregate blood glucose measurementassociated with the selected time interval. The dosing controller mayalso include determining an adjustment factor for adjusting a nextrecommended carbohydrate-to-insulin ratio governed by the selected timeinterval based on the selected governing blood glucose measurement,obtaining a previous day's recommended carbohydrate-to-insulin ratiogoverned by the selected time interval, and determining the nextrecommended carbohydrate-to-insulin ratio by multiplying the adjustmentfactor times the previous day's recommended carbohydrate-to-insulinratio. The selected time interval may include one of lunch blood glucosetime interval, a dinner blood glucose time interval, or a bedtime bloodglucose time interval. Each scheduled blood glucose time interval maycorrelate to an associated blood glucose type including one of apre-breakfast blood glucose measurement, a pre-lunch blood glucosemeasurement, a pre-dinner blood glucose measurement, a bedtime bloodglucose measurement and a midsleep blood glucose measurement. The dosingcontroller may determine the blood glucose type for each of bloodglucose measurement. The blood glucose type is tagged by the patientwhen measuring the blood glucose measurement.

A portion of the scheduled blood glucose time intervals may beassociated with time intervals when the patient is consuming meals and aremaining portion of the scheduled blood glucose time intervals may beassociated with time intervals when the patient is not consuming meals.The dosing controller may receive a specified date range from a remotehealthcare provider computing device in communication with the dataprocessing hardware and aggregate one or more of the blood glucosemeasurements associated with at least one scheduled blood glucose timeintervals and within the specified date range. The representativeaggregate blood glucose measurement may include a mean blood glucosevalue for the associated scheduled blood glucose time interval. Therepresentative blood glucose measurement may also include a median bloodglucose value for the associated scheduled blood glucose time interval.

The details of one or more implementations of the disclosure are setforth in the accompanying drawings and the description below. Otheraspects, features, and advantages will be apparent from the descriptionand drawings, and from the claims.

DESCRIPTION OF DRAWINGS

FIG. 1A is a schematic view of an exemplary system for monitoring bloodglucose level of a patient.

FIG. 1B is a schematic view of an exemplary system for monitoring bloodglucose level of a patient.

FIG. 1C is a schematic view of an exemplary administration device incommunication with a dosing controller.

FIG. 2A is a schematic view of an exemplary program for monitoring theblood glucose level of a patient.

FIG. 2B is a schematic view of an exemplary display for inputtingpatient information.

FIG. 2C-2F are schematic views of an exemplary display for inputtingSubQ information relating to the patient.

FIG. 2G is a schematic view of an input screen for inputtingconfigurable constants.

FIG. 2H is a schematic view of an input screen for inputtingtime-boundaries for intervals within a day.

FIG. 3 is a schematic view of an exemplary correction boluses process.

FIG. 4 is a schematic view of an exemplary adjustment factor process.

FIG. 5A is a schematic view of an outpatient process using a mobiledevice capable of measuring blood glucose.

FIG. 5B is a schematic view of an outpatient process using mobile devicecapable of measuring blood glucose and calculating a corrective bolus ofinsulin.

FIG. 6A shows a data transfer process for communicating blood glucosedata measured by a patient's glucometer.

FIG. 6B shows a process for determining an amount of past blood glucosedata for use in adjusting dosages of insulin.

FIG. 6C shows a process for correcting flagged blood glucosemeasurements to reflect an actual time of the blood glucose measurement.

FIGS. 7A-7C are schematic views of a blood glucose aggregation processfor time intervals when a patient is not consuming meals.

FIGS. 7D-7F are schematic views of a blood glucose aggregation processfor time intervals when a patient is consuming meals.

FIG. 8 is a schematic view of an exemplary basal adjustment process

FIG. 9 is a schematic view of an exemplary meal bolus adjustmentprocess.

FIG. 10 is a schematic view of an exemplary carbohydrate-insulin-ratioadjustment process.

FIG. 11 is a schematic view of exemplary components of the system ofFIGS. 1A-1C.

FIG. 12A is a schematic view of an exemplary display for viewing bloodglucose data.

FIG. 12B is a schematic detailed view of an exemplary modal day scatterchart for viewing blood glucose data.

FIG. 13 is a schematic view of an exemplary carbohydrate-insulin-ratioadjustment process on a meal-by-meal basis.

FIG. 14 is an exemplary arrangement of operations for administeringinsulin.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION

Diabetic outpatients must manage their blood glucose level withindesired ranges by using insulin therapy that includes injection dosagesof insulin corresponding to meal boluses and basal dosages. Meal boluseswithout meals cause hypoglycemia; meals without meal boluses causehyperglycemia. Different providers may use different methods ofadjusting doses: some may use formulas of their own; some may use paperprotocols that are complex and difficult for the outpatient to follow,leading to a high incidence of human error; and some may use heuristicmethods. Therefore, it is desirable to have a clinical support system100 (FIGS. 1A and 1B) that monitors outpatients' blood glucose level.

Referring to FIG. 1A-1B, in some implementations, a clinical decisionsupport system 100 analyzes inputted patient condition parameters for anoutpatient 10 and calculates a personalized dose of insulin to bring andmaintain the patient's blood glucose level into a target range BG_(TR).As used herein, the patient 10 refers to an outpatient that may belocated at some remote location, such as the patient's 10 residence orplace of employment. As used herein, the term “clinical” may refer to ahospital call center. Moreover, the system 100 monitors the glucoselevels of a patient 10 and calculates a recommended subcutaneous insulindose to bring the patient's blood glucose into the preferred targetrange BG_(TR) over a recommended period of time. A qualified and trainedhealthcare professional 40 may use the system 100 along with clinicalreasoning to determine the proper dosing administered to a patient 10.Therefore, the system 100 is a glycemic management tool for evaluation apatient's current and cumulative blood glucose value BG while takinginto consideration the patient's information such as age, weight, andheight. The system 100 may also consider other information such ascarbohydrate content of meals, insulin doses being administered to thepatient 10, e.g., long-acting insulin doses for basal insulin andrapid-acting insulin doses for meal boluses and correction boluses.Based on those measurements (that may be stored in non-transitory memory24, 114, 144), the system 100 recommends a subcutaneous basal and bolusinsulin dosing recommendation or prescribed dose to adjust and maintainthe blood glucose level towards a configurable (based on the patient'sinformation) physician's determined blood glucose target range BG_(TR).The system 100 also considers a patient's insulin sensitivity orimproved glycemic management and outcomes. The system 100 may take intoaccount pertinent patient information such as demographics and previousresults, leading to a more efficient use of healthcare resources.Finally, the system 100 provides a reporting platform for reporting therecommendations or prescribed dose(s) to the user 40 and the patient 10.In addition, the system 100 provides faster, more reliable, and moreefficient insulin administration than a human monitoring the insulinadministration. The system 100 reduces the probability of human errorand insures consistent treatment, due to the system's capability ofstoring and tracking the patient's blood glucose levels BG, which may beused for statistical studies. The system 100 provides a meal-by-mealadjustment of Meal Boluses without carbohydrate counting, by providing adedicated subprogram that adjusts meal boluses based on the immediatelypreceding meal bolus and the BG that followed it. The system 100provides a meal-by-meal adjustment of Meal Boluses with carbohydratecounting by providing a dedicated subprogram that adjusts meal bolusesbased a Carbohydrate-to-Insulin Ratio (CIR) that is adjusted at eachmeal, based on the CIR used at the immediately preceding meal bolus andthe BG that followed it.

Hyperglycemia is a condition that exists when blood sugars are too high.While hyperglycemia is typically associated with diabetes, thiscondition can exist in many patients who do not have diabetes, yet haveelevated blood sugar levels caused by trauma or stress from surgery andother complications from hospital procedures. Insulin therapy is used tobring blood sugar levels back into a normal range.

Hypoglycemia may occur at any time when a patient's blood glucose levelis below a preferred target. Appropriate management of blood glucoselevels for critically ill patients reduces co-morbidities and isassociated with a decrease in infection rates, length of hospital stay,and death. The treatment of hyperglycemia may differ depending onwhether or not a patient has been diagnosed with Type 1 diabetesmellitus, Type 2 diabetes mellitus, gestational diabetes mellitus, ornon-diabetic stress hyperglycemia. The blood glucose target rangeBG_(TR) is defined by a lower limit, i.e., a low target BG_(TRL) and anupper limit, i.e., a high target BG_(TRH).

Diabetes Mellitus has been treated for many years with insulin. Somerecurring terms and phrases are described below:

Injection: Administering insulin by means of manual syringe or aninsulin “pen,” with a portable syringe named for its resemblance to thefamiliar writing implement.

Infusion: Administering insulin in a continuous manner by means of aninsulin pump for subcutaneous insulin apparatus 123 a capable ofcontinuous administration.

Basal-Bolus Therapy: Basal-bolus therapy is a term that collectivelyrefers to any insulin regimen involving basal insulin and boluses ofinsulin.

Basal Insulin: Insulin that is intended to metabolize the glucosereleased by a patient's the liver during a fasting state. Basal insulinis administered in such a way that it maintains a background level ofinsulin in the patient's blood, which is generally steady but may bevaried in a programmed manner by an insulin pump 123 a. Basal insulin isa slow, relatively continuous supply of insulin throughout the day andnight that provides the low, but present, insulin concentrationnecessary to balance glucose consumption (glucose uptake and oxidation)and glucose production (glucogenolysis and gluconeogenesis). A patient'sBasal insulin needs are usually about 10 to 15 mU/kg/hr and account for30% to 50% of the total daily insulin needs; however, considerablevariation occurs based on the patient 10.

Bolus Insulin: Insulin that is administered in discrete doses. There aretwo main types of boluses, Meal Bolus and Correction Bolus.

Meal Bolus: Taken just before a meal in an amount which is proportionalto the anticipated immediate effect of carbohydrates in the mealentering the blood directly from the digestive system. The amounts ofthe Meal Boluses may be determined and prescribed by a physician 40 foreach meal during the day, i.e., breakfast, lunch, and dinner.Alternatively, the Meal Bolus may be calculated in an amount generallyproportional to the number of grams of carbohydrates in the meal. Theamount of the Meal Bolus is calculated using a proportionality constant,which is a personalized number called the Carbohydrate-to-Insulin Ratio(CIR) and calculated as follows:Meal Insulin Bolus={grams of carbohydrates in the meal}/CIR  (1)

Correction Bolus CB: Injected immediately after a blood glucosemeasurement; the amount of the correction bolus is proportional to theerror in the BG (i.e., the bolus is proportional to the differencebetween the blood glucose measurement BG and the patient's personalizedTarget blood glucose BG_(Target)). The proportionality constant is apersonalized number called the Correction Factor, CF. The CorrectionBolus is calculated as follows:CB=(BG−BG_(Target))/CF  (2)

A Correction Bolus CB is generally administered in a fasting state,after the previously consumed meal has been digested. This oftencoincides with the time just before the next meal.

In some implementations, blood glucose measurements BG are aggregatedusing an exponentially-weighted moving average EMA_(t) as a function foreach modal day's time interval BG. The EMAt is calculated as follows:EMA_(t)=α(BG_(t))+(1−α)EMA_(t-1),  (3)wherein:α=2/(n+1),wherein n is the number of equivalent days averaged. In otherembodiments, an arithmetic moving average is utilized that calculatesthe sum of all BG values in n days divided by a total count (n) of allvalues associated with the arithmetic average.

There are several kinds of Basal-Bolus insulin therapy including InsulinPump therapy and Multiple Dose Injection therapy:

Insulin Pump Therapy: An insulin pump 123 a is a medical device used forthe administration of insulin in the treatment of diabetes mellitus,also known as continuous subcutaneous insulin infusion therapy. Thedevice includes: a pump, a disposable reservoir for insulin, and adisposable infusion set. The pump 123 a is an alternative to multipledaily injections of insulin by insulin syringe or an insulin pen andallows for intensive insulin therapy when used in conjunction with bloodglucose monitoring and carbohydrate counting. The insulin pump 123 a isa battery-powered device about the size of a pager. It contains acartridge of insulin, and it pumps the insulin into the patient via an“infusion set”, which is a small plastic needle or “canula” fitted withan adhesive patch. Only rapid-acting insulin is used.

Multiple Dose Injection (MDI): MDI involves the subcutaneous manualinjection of insulin several times per day using syringes or insulinpens 123 b. Meal insulin is supplied by injection of rapid-actinginsulin before each meal in an amount proportional to the meal. Basalinsulin is provided as a once, twice, or three time daily injection of adose of long-acting insulin. Other dosage frequencies may be available.Advances continue to be made in developing different types of insulin,many of which are used to great advantage with MDI regimens:

Long-acting insulins are non-peaking and can be injected as infrequentlyas once per day. These insulins are widely used for Basal Insulin. Theyare administered in dosages that make them appropriate for the fastingstate of the patient, in which the blood glucose is replenished by theliver to maintain a steady minimum blood glucose level.

Rapid-acting insulins act on a time scale shorter than natural insulin.They are appropriate for boluses.

The decision support system 100 includes a glycemic management module50, an integration module 60, a surveillance module 70, and a reportingmodule 80. Each module 50, 60, 70, 80 is in communication with the othermodules 50, 60, 70, 80 via a network 20. In some examples, the network20 (discussed below) provides access to cloud computing resources thatallows for the performance of services on remote devices instead of thespecific modules 50, 60, 70, 80. The glycemic management module 50executes a program 200 (e.g., an executable instruction set) on aprocessor 112, 132, 142 or on the cloud computing resources. Theintegration module 60 allows for the interaction of users 40 andpatients 10 with the system 100. The integration module 60 receivesinformation inputted by a user 40 and allows the user 40 to retrievepreviously inputted information stored on a storage system (e.g., one ormore of cloud storage resources 24, a non-transitory memory 144 of anelectronic medical system 140 of a clinic 42 or hospital call center(e.g., Telemedicine facility), a non-transitory memory 114 of thepatient device 110, a non-transitory memory 134 of the serviceprovider's system 130, or other non-transitory storage media incommunication with the integration module 60). Therefore, theintegration module 60 allows for the interaction between the users 40,patients 10, and the system 100 via a display 116, 146. The surveillancemodule 70 considers patient information 208 a received from a user 40via the integration module 60 and information received from a glucometer124 that measures a patient's blood glucose value BG and determines ifthe patient 10 is within a threshold blood glucose value BG_(TH). Insome examples, the surveillance module 70 alerts the user 40 if apatient's blood glucose values BG are not within a threshold bloodglucose value BG_(TH). The surveillance module 70 may be preconfiguredto alert the user 40 of other discrepancies between expected values andactual values based on pre-configured parameters (discussed below). Forexample, when a patient's blood glucose value BG drops below a lowerlimit of the threshold blood glucose value BG_(THL). The reportingmodule 80 may be in communication with at least one display 116, 146 andprovides information to the user 40 determined using the glycemicmanagement module 50, the integration module 60, and/or the surveillancemodule 70. In some examples, the reporting module 80 provides a reportthat may be displayed on a display 116, 146 and/or is capable of beingprinted.

The system 100 is configured to evaluate a glucose level and nutritionalintake of a patient 10. Based on the evaluation and analysis of thedata, the system 100 calculates an insulin dose, which is administeredto the patient 10 to bring and maintain the blood glucose level of thepatient 10 into the blood glucose target range BG R. The system 100 maybe applied to various devices, including, but not limited to,subcutaneous insulin infusion pumps 123 a, insulin pens 123 b,glucometers 124, continuous glucose monitoring systems, and glucosesensors.

In some examples the clinical decision support system 100 includes anetwork 20, a patient device 110, a dosing controller 160, a serviceprovider 130, and a meter manufacturer provider 190. The patient device110 may include, but is not limited to, desktop computers 110 a orportable electronic device 110 b (e.g., cellular phone, smartphone,personal digital assistant, barcode reader, personal computer, or awireless pad) or any other electronic device capable of sending andreceiving information via the network 20. In some implementations, oneor more of the patient's glucometer 124, insulin pump 123 a, or insulinpen 123 b are capable of sending and receiving information via thenetwork 20.

The patient device 110 a, 110 b includes a data processor 112 a, 112 b(e.g., a computing device that executes instructions), andnon-transitory memory 114 a, 114 b and a display 116 a, 116 b (e.g.,touch display or non-touch display) in communication with the dataprocessor 112. In some examples, the patient device 110 includes akeyboard 118, speakers 122, microphones, mouse, and a camera.

The glucometer 124, insulin pump 123 a, and insulin pen 123 b associatedwith the patient 10 include a data processor 112 c, 112 d, 112 e (e.g.,a computing device that executes instructions), and non-transitorymemory 114 c, 114 d, 114 e and a display 116 c, 116 d, 116 e (e.g.,touch display or non-touch display in communication with the dataprocessor 112 c, 112 d, 112 e.

The meter manufacturer provider 190 may include may include a dataprocessor 192 in communication with non-transitory memory 194. The dataprocessor 192 may execute a proprietary download program 196 fordownloading blood glucose BG data from the memory 114 c of the patient'sglucometer 124. In some implementations, the proprietary downloadprogram 196 is implemented on the health care provider's 140 computingdevice 142 or the patient's 10 device 110 a for downloading the BG datafrom memory 114 c. In some examples, the download program 196 exports aBG data file for storage in the non-transitory memory 24, 114, 144. Thedata processor 192 may further execute a web-based application 198 forreceiving and formatting BG data transmitted from one or more of thepatient's devices 110 a, 110 b, 124, 123 a, 123 b and storing the BGdata in non-transitory memory 24, 114, 144.

The service provider 130 may include a data processor 132 incommunication with non-transitory memory 134. The service provider 130provides the patient 10 with a program 200 (see FIG. 2) (e.g., a mobileapplication, a web-site application, or a downloadable program thatincludes a set of instructions) executable on a processor 112, 132, 142of the dosing controller 160 and accessible through the network 20 viathe patient device 110, health care provider electronic medical recordsystems 140, portable blood glucose measurement devices 124 (e.g.,glucose meter or glucometer), or portable administration devices 123 a,123 b.

In some implementations, a health care provider medical record system140 is located at a doctor's office, clinic 42, or a facilityadministered by a hospital (such as a hospital call center (HCP)) andincludes a data processor 142, a non-transitory memory 144, and adisplay 146 (e.g., touch display or non-touch display). Thenon-transitory memory 144 and the display 146 are in communication withthe data processor 142. In some examples, the health care providerelectronic medical system 140 includes a keyboard 148 in communicationwith the data processor 142 to allow a user 40 to input data, such aspatient information 208 a (FIGS. 2A and 2B). The non-transitory memory144 maintains patient records capable of being retrieved, viewed, and,in some examples, modified and updated by authorized hospital personalon the display 146.

The dosing controller 160 is in communication with the glucometer 124,insulin administration device 123 a, 123 b and includes a computingdevice 112, 132, 142 and non-transitory memory 114, 134, 144 incommunication with the computing device 112, 132, 142. The dosingcontroller 160 executes the program 200. The dosing controller 160stores patient related information retrieved from the glucometer 124 todetermine insulin doses and dosing parameters based on the receivedblood glucose measurement BG.

Referring to FIG. 1C, in some implementations, the insulin device 123(e.g., administration device), in communication with the dosingcontroller 160, capable of executing instructions for administeringinsulin according to a subcutaneous insulin treatment program selectedby the dosing controller 160. The administration device 123 may includethe insulin pump 123 a or the pen 123 b. The administration device 123is in communication with the glucometer 124 and includes a computingdevice 112 d, 112 e and non-transitory memory 114 d, 114 e incommunication with the computing device 112 d, 112 e. The administrationdevice 123 includes a doser 223 a, 223 b in communication with theadministration computing device 112 d, 112 e for administering insulinto the patient. For instance, the doser 223 a of the insulin pump 123 aincludes an infusion set including a tube in fluid communication with aninsulin reservoir and a cannula inserted into the patient's 10 body andsecured via an adhesive patch. The doser 223 b of the pen 123 b includesa needle for insertion into the patients 10 for administering insulinfrom an insulin cartridge. The administration device 123 may receive asubcutaneous insulin treatment program selected by and transmitted fromthe dosing controller 160, while the administration computing device 112d, 112 e may execute the subcutaneous insulin treatment program.Executing the subcutaneous insulin treatment program by theadministration computing device 112 d, 112 e causes the doser 223 a, 223b to administer doses of insulin specified by the subcutaneous insulintreatment program. For instance, units for the doses of insulin may beautomatically set or dialed in by the administration device 123 a, 123 band administered via the doser 223 a, 223 b to the patient 10.Accordingly, the administration devices 123 a, 123 b may be “smart”administration devices capable of communicating with the dosingcontroller 160 to populate recommended doses of insulin foradministering to the patient 10. In some examples, the administrationdevices 123 a, 123 b may execute the dosing controller 160 on theadministration computing devices 112 d, 112 e to calculate therecommended doses of insulin for administering to the patient 10.

The network 20 may include any type of network that allows sending andreceiving communication signals, such as a wireless telecommunicationnetwork, a cellular telephone network, a time division multiple access(TDMA) network, a code division multiple access (CDMA) network, Globalsystem for mobile communications (GSM), a third generation (3G) network,fourth generation (4G) network, a satellite communications network, andother communication networks. The network 20 may include one or more ofa Wide Area Network (WAN), a Local Area Network (LAN), and a PersonalArea Network (PAN). In some examples, the network 20 includes acombination of data networks, telecommunication networks, and acombination of data and telecommunication networks. The patient device110, the service provider 130, and the hospital electronic medicalrecord system 140 communicate with each other by sending and receivingsignals (wired or wireless) via the network 20. In some examples, thenetwork 20 provides access to cloud computing resources, which may beelastic/on-demand computing and/or storage resources 24 available overthe network 20. The term ‘cloud’ services generally refers to a serviceperformed not locally on a user's device, but rather delivered from oneor more remote devices accessible via one or more networks 20.

Referring to FIGS. 1B and 2A-2F, the program 200 receives parameters(e.g., patient condition parameters) inputted via the client device 110,the service provider 130, and/or the clinic system 140, analyzes theinputted parameters, and determines a personalized dose of insulin tobring and maintain a patient's blood glucose level BG into a preferredtarget range BG_(TR) for a SubQ outpatient program 200 (FIG. 2A).

In some implementations, before the program 200 begins to receive theparameters, the program 200 may receive a username and a password (e.g.,at a login screen displayed on the display 116, 146) to verify that aqualified and trained healthcare professional 40 is initiating theprogram 200 and entering the correct information that the program 200needs to accurately administer insulin to the patient 10. The system 100may customize the login screen to allow a user 40 to reset theirpassword and/or username. Moreover, the system 100 may provide a logoutbutton (not shown) that allows the user 40 to log out of the system 100.The logout button may be displayed on the display 116, 146 at any timeduring the execution of the program 200.

The decision support system 100 may include an alarm system 120 thatalerts a user 40 at the clinic 42 (or hospital call center) when thepatient's blood glucose level BG is outside the target range BG_(TR).The alarm system 120 may produce an audible sound via speaker 122 in theform of a beep or some like audio sounding mechanism. For instance, thealarm system 120 may produce an anudible sound via a speaker 122 of themobile device 110 b In some examples, the alarm system 120 displays awarning message or other type of indication on the display 116 a-e ofthe patient device 110 to provide a warning message. The alarm system120 may also send the audible and/or visual notification via the network20 to the clinic system 140 (or any other remote station) for display onthe display 146 of the clinic system 140 or played through speakers 152of the clinic system 140.

For commencing a SubQ outpatient process 1800 (FIGS. 5A and 5B), theprogram 200 prompts a user 40 to input patient information 208 a atblock 208. The user 40 may input the patient information 208 a, forexample, via the user device 140 or via the health care provider medicalrecord systems 140 located at a clinic 42 (or a doctor's office or HCP).The user 40 may input new patient information 208 a as shown in FIG. 2B.The program 200 may retrieve the patient information 208 a from thenon-transitory memory 144 of the clinic's electronic medical system 140or the non-transitory memory 114 of the patient device 110 (e.g., wherethe patient information 208 a was previously entered and stored). Thepatient information 208 a may include, but is not limited to, apatient's name, a patient's identification number (ID), a patient'sheight, weight, date of birth, diabetes history, physician name,emergency contact, hospital unit, diagnosis, gender, room number, andany other relevant information.

Referring to FIGS. 2A and 2C-2F, the program 200 at block 216 furtherrequests the user 40 to enter SubQ information 216 a for the patient 10,such as patient diabetes status, subcutaneous Orderset Type ordered forthe patient 10 (e.g., “Fixed Carbs/meal” that is intended for patientson a consistent carbohydrate diet, total daily dosage (TDD), bolusinsulin type (e.g., Novolog), basil insulin type (e.g., Lantus) andfrequency of distribution (e.g., 1 dose per day, 2 doses per day, 3doses per day, etc.), basil time, basal percentage of TDD, meal boluspercentage of TDD, daily meal bolus distribution (e.g., breakfast bolus,lunch bolus and dinner bolus), or any other relevant information. Insome implementations, TDD is calculated following a period onIntravenous Insulin in accordance with equation:TDD=QuickTransitionConstant*M _(Trans)  (4A)where QuickTransitionConstant is usually equal to 1000, and M_(Trans) isthe patient's multiplier at the time of initiation of the SubQtransition process. In other implementations, the TDD is calculated by astatistical correlation of TDD as a function of body weight. Thefollowing equation is the correlation used:TDD=0.5*Weight(kg)  (4B)In other implementations, the patient's total daily dose TDD iscalculated in accordance with the following equation:TDD=(BG_(Target) −K)*(M _(Trans))*24  (4C)where M_(Trans) is the patient's multiplier at the time of initiation ofthe SubQ transition process.

In some implementations, the patient SubQ information 216 a isprepopulated with default parameters, which may be adjusted or modified.In some examples, portions of the patient SubQ information 216 areprepopulated with previously entered patient subcutaneous information216 a. The program 200 may prompt the request to the user 40 to enterthe SubQ information 216 a on the display 116 of the patient device 110.In some implementations, the subcutaneous insulin process 1800 promptsthe request on the display 116 for a custom start of new SubQ patients(FIG. 2C) being treated with the SubQ outpatient process 1800. In someexamples, the program 200 prompts the request on the display 116 for aweight-based start of SubQ patients being treated with the SubQoutpatient process 1800 as shown in FIG. 2D. For instance, the user 40may input the weight (e.g., 108 kg) of the patient 10, and in someexamples, the TDD is calculated using EQ. 4B based on the patient'sweight. As shown in FIG. 2E, the user 40 may further enter a schedulefor when blood glucose BG measurements are required 430 (e.g., Next BGDue: Lunch) for the patient 10 and whether or not an alarm 434 is to beactivated. For instance, if a BG measurement is below a threshold value,or if the patient has not submitted a BG measurement during Lunch, thealarm system 120 may generate a warning sound via speakers 122 to alertthe patient 10 that a BG measurement is required. The alarm may sound onone or more of the patient's portable devices 110 a, 110 b, 124, 123 a,123 b. As shown in FIG. 2F, the patient 10 may enter a number ofcarbohydrates for the upcoming meal (e.g., 60) such that adjustment ofMeal Boluses with carbohydrate counting can be calculated by EQ. 1 basedupon the Carbohydrate-to-Insulin Ratio (CIR). In some implementations,the CIR is associated with the BGtype or Bucket, and adjusted on a dailybasis by process 2500 (FIG. 10). In other implementations, the CIR isadjusted at each meal, based on the CIR used at the immediatelypreceding meal bolus and the BG measurement occurring after that mealbolus by process 2600 (FIG. 13).

The program 200 flows to block 216, where the user 40 enters patientsubcutaneous information 216 a, such as bolus insulin type, targetrange, basal insulin type and frequency of distribution (e.g., 1 doseper day, 2 doses per day, 3 doses per day, etc.), patient diabetesstatus, subcutaneous type ordered for the patient (e.g., Basal/Bolus andcorrection that is intended for patients on a consistent carbohydratediet, frequency of patient blood glucose measurements, or any otherrelevant information. In some implementations, the patient subcutaneousinformation 216 a is prepopulated with default parameters, which may beadjusted or modified. When the user 40 enters the patient subcutaneousinformation 216 a, the user selects the program 200 to execute the SubQoutpatient process 1800 at block 226.

In some implementations, the user 40 selects to initiate a subcutaneousoutpatient program 200 (FIG. 2A) executing on the dosing controller 160to provide recommended insulin dosing (bolus/basal) for a patient 10equipped with one or more portable devices 110 a, 110 b, 124, 123 a, 123b. The user 40 may configure the subcutaneous outpatient program 200 byselecting the portable devices used by the patient 10. Selection ofblock 124 indicates information for the patient's glucometer 124,including communication capabilities with other devices and/or thenetwork 20. Selection of block 123 b indicates that the patient 10 usesan insulin pen 123 b for administering insulin. Information for the pen123 b may be provided that includes communication capabilities withother devices and/or the network 20. In some examples, the pen 123 b isa “smart” that may include an administration computing device 112 e incommunication with the dosing controller 160 for administering insulinto the patient 10. Selection of block 123 a indicates that the patient10 uses an insulin pump 123 a for administering insulin. Information forthe pump 123 a may be provided that includes communication capabilitieswith other devices and/or the network 20. In some examples, the pen 123b is a “smart” pen that may include the administration computing device112 d in communication with the dosing controller 160 for administeringinsulin to the patient 10. Selection of block 110 b indicatesinformation for the patient's 10 smartphone 110 b or tablet, includingcommunication capabilities with the glucometer 124 and/or the insulinadministration devices 123 a,123 b, For instance, the smartphone 110 bmay communicate with the glucometer 124 via Bluetooth or otherconnection to download BG data from the memory 114 c of the glucometer,and transmit the downloaded BG data through the network 20. In otherexamples, the smartphone 110 b may receive recommended insulin dosesover the network 20 from the dosing controller 160 and provide therecommended insulin doses to the glucometer 124 and/or insulinadministration device 123 a, 123 b.

In some implementations, some functions or processes are used within theSubQ outpatient program 200 (FIG. 2) and SubQ outpatient process 1800(FIGS. 5A and 5B) such as determining the general and pre-mealcorrection (FIG. 3), determining the adjustment factor AF (FIG. 4), andhypoglycemia treatment.

Referring to FIG. 3, correction boluses CB are used in the SubQoutpatient program 200 (FIG. 2) and process 1800 (FIG. 5A) (FIG. 5B);because of this, correction boluses CB may be incorporated into afunction having variables such as the blood glucose measurement BG of apatient 10, a patient's personalized target blood glucose BG_(Target),and a correction factor CF. Thus, correction boluses CB are described asa function of the blood glucose measurement BG, the target blood glucoseBG_(Target), and the correction factor CF (see EQ. 7 below). The process700 calculates the correction bolus CB immediately after a blood glucosevalue BG of a patient 10 is measured. Once a calculation of thecorrection bolus CB is completed, the patient 10 administers thecorrection bolus CB to the patient 10, right after the blood glucosevalue BG is measured and used to calculate the correction bolus CB.

In some examples, the process 700 may determine the total daily dose TDDof insulin once per day, for example, every night at midnight or at thenext opening of the given patient's record after midnight. Other timesmay also be available. In addition, the total daily dose TDD may becalculated more frequently during the day, in some examples, the totaldaily dose TDD is calculated more frequently and considers the totaldaily dose TDD within the past 24 hours. The process 700 provides atimer 702, such as a countdown timer 702, where the timer 702 determinesthe time the process 700 executes. The timer 702 may be a count up timeror any other kind of timer. When the timer 702 reaches its expiration orreaches a certain time (e.g., zero for a countdown timer 702), the timer702 executes the process 700. The counter 702 is used to determine atwhat time the process 700, at block 704, calculates the total daily doseTDD. If the counter is set to 24 hours for example, then decision block704 checks if the time has reached 24 hours, and when it does, then theprocess 700 calculates the total daily dose TDD of insulin. Block 706may receive insulin dosing data from a merged database 1906 (FIG. 6A)within the non-transitory memory 24, 114, 144 via Entry Point T. Thecorrection bolus process 700 determines a total daily dose of insulinTDD, based on the following equation:TDD=Sum over previous day(all basal+all meal boluses+all correctionboluses)  (5A)For some configurations, the TDD is calculated as the sum of the latestrecommended insulin doses:Alternate TDD=Sum of(latest recommended basal+latest recommendedBreakfast Bolus+Latest Recommended Lunch Bolus+Latest Recommended DinnerBolus)  (5B)

After the process 700 determines the total daily dose TDD of insulin atblock 706, the process 700 determines a Correction Factor CF immediatelythereafter at block 710, using the calculated total daily dose TDD fromblock 706 and EQ. 5. The correction factor CF is determined using thefollowing equation:CF=CFR/TDD  (6)where CFR is a configurable constant stored in the non-transitory memory24, 114, 144 of the system and can be changed from the setup screen(FIG. 2D). At block 708, the process 700 retrieves the configurableconstant CFR value from the non-transitory memory 24, 114, 144 tocalculate the correction factor CF at block 710. The configurableconstant CFR is determined from a published statistical correlation andis configurable by the hospital, nurses and doctors. The flexibility ofmodifying the correction constant CF, gives the system 100 flexibilitywhen a new published configurable constant CFR is more accurate than theone being used. In some examples, the configurable constant CFR is aconfigurable constant set to 1700, other values may also be available.In some examples, the total daily dose TDD and CF are determined onceper day (e.g., at or soon after midnight).

Once the correction factor CF is determined in EQ. 6, the process 700determines the correction bolus insulin dose at block 714 using thefollowing equation:CB=(BG−BG_(Target))/CF)  (7)where BG is the blood glucose measurement of a patient 10 retrieved atblock 712, BG_(Target) is the patient's personalized Target bloodglucose, and CF is the correction factor. The process 700 returns thecorrection bolus CB at block 716. Rapid-acting analog insulin iscurrently used for Correction Boluses because it responds quickly to ahigh blood glucose BG. Also rapid acting analog insulin is currentlyused for meal boluses; it is usually taken just before or with a meal(injected or delivered via a pump). Rapid-acting analog insulin actsvery quickly to minimize the rise of patient's blood sugar which followseating.

A Correction Bolus CB is calculated for a blood glucose value BG at anytime during the program 200. Pre-meal Correction Boluses CB, arecalculated using EQ. 7. In the Pre-meal Correction Bolus equation (7)there is no need to account for Remaining Insulin I_(Rem) becausesufficient time has passed for almost all of the previous meal bolus tobe depleted. However, post-prandial correction boluses (after-mealcorrection boluses) are employed much sooner after the recent meal bolusand use different calculations that account for remaining insulinI_(Rem) that remains in the patient's body after a recent meal bolus.Rapid-acting analog insulin is generally removed by a body's naturalmechanisms at a rate proportional to the insulin remaining I_(Rem) inthe patient's body, causing the remaining insulin I_(Rem) in thepatient's body to exhibit a negative exponential time-curve.Manufacturers provide data as to the lifetime of their insulinformulations. The data usually includes a half-life or mean lifetime ofthe rapid-acting analog insulin. The half-life of the rapid-actinganalog insulin may be converted to mean lifetime by the conversionformula:mean lifetime=Half-life*ln(2)  (8A)where ln(2) is the natural logarithm {base e} of two.

In some implementations, the process 700 accounts for post-prandialcorrection boluses by determining if there is any remaining insulinI_(Rem) in the patient's body to exhibit a negative exponentialtime-curve. At block 718, process 700 initializes a loop for determiningI_(Rem) by setting I_(Rem) equal to zero, and retrieves a next earlierinsulin dose (Dprev) and the associated data-time (T_(Dose)) at block720.

The brand of insulin being used is associated with two half-lifeparameters: the half-life of the insulin activity (HLact) and thehalf-life of the process of diffusion of the insulin from the injectionsite into the blood (HLinj). Since the manufacturers and brands ofinsulin are few, the program 200 maintains the two half-lives of eachinsulin brand as configurable constants. These configurable constantscan be input by a healthcare provider using an input screen of FIG. 2G.For instance, the display 146 of the healthcare provider computingsystem 140 can display the input screen 2000 to enable the healthcareprovider to input the configurable constants.

For a single previous dose of insulin Dprev, given at a time T_(Dose),the insulin remaining in the patient's body at the current timeT_(Current) refers to the Remaining Insulin I_(Rem). The derivation ofthe equation for IRem involves a time-dependent two-compartment model ofinsulin: The insulin in the injection site Iinj(t) and the “active”insulin in the blood and cell membrane, Iact(t). The differentialequation for Iinj(t) is:dIinj/dt=−(0.693/HLinj)*Iinj(t).  (8B)

The differential equation for Iact(t) is:dIact(t)/dt=(0.693/HLinj)*Iinj(t)−(0.693/HLact)*Iact(t)  (8C)

Equations 8B and 8C are simultaneous linear first-order differentialequations. The solutions must be added together to represent the totalinsulin remaining, I_(Rem). The final result can be written as atime-dependent factor that can be multiplied by the initial dose Dprevto obtain the time-dependent total remaining insulin I_(Rem).

Process 700 determines, at block 724, I_(Rem) by multiplying theprevious single dose of insulin Dprev {e.g. a Meal Bolus, CorrectionBolus, or combined bolus} times a time-dependent factor as follows:I _(Rem)(single dose)=Dprev*EXP(−(T _(current) −T_(Dose))*0.693/HLinj)+D0*0.693/HLinj/(0.693/HLact−0.693/HLinj)+Dprev*(EXP(−(T_(current) −T _(Dose))*0.693/HLinj)−EXP(−(T _(current) −T_(Dose))*0.693/HLact))  (9A)

The Remaining Insulin I_(Rem) may account for multiple previous dosesoccurring in a time window looking backwards within a lifetime of theinsulin being used. For example, I_(Rem) may be associated with aconfigurable constant within the range of 4 to 7 hours that representsthe lifetime of rapid analog insulin. For example, I_(Rem) may bedetermined as follows:I _(Rem)=sum of [I _(Rem)(single dose)over all doses in the within thelifetime of the insulin being used]  (9B)

Process 700 iteratively determines I_(Rem) in the loop until, at block722, the difference between the current time T_(Current) and the time atwhich the bolus was administered T_(Dose) is greater than a time relatedto the lifetime of the insulin used. Thus, when block 722 is “NO”,process 700 calculates, at block 714, a post meal correction bolusCBpost that deducts the remaining insulin I_(Rem) in the patient's bodyas follows:

$\begin{matrix}{{CB}_{post} = {\frac{\left( {{BG} - {BG}_{Target}} \right)}{CF} - I_{Rem}}} & (10)\end{matrix}$

In some examples, Post Meal Correction doses CB_(post) (EQ. 10) aretaken into consideration only if they are positive (units of insulin),which means a negative value post meal correction bolus CB_(post) cannotbe used to reduce the meal bolus portion of a new combined bolus.

Referring to FIG. 4, process 800 describes a function that determines anAdjustment Factor AF based on an input of a Governing BloodGlucoseBGgov. The Adjustment Factor AF is used by the SubQ outpatientprocess 1800 (FIGS. 5A and 5B) for calculating a next recommended basaldose using a basal adjustment process 2300 (FIG. 8), for calculatingnext recommended meal boluses (e.g., Breakfast, Lunch, and DinnerBoluses) using a meal bolus adjustment process 2400 (FIG. 9), and forcalculating a next recommended Carbohydrate-Insulin-Ratio (CIR) usingCIR adjustment process 2500 (FIG. 10). An insulin adjustment process2300, 2400, applied to Basal doses and Meal Boluses, determines anadjusted Recommended Basal dose RecBasal, or a Recommended Meal BolusRecMealBol, by applying a unit-less Adjustment Factor AF to thepreceding recommendation of the same dose, RecBasal_(prev), orRecMealBol_(prev). All dose adjustments are governed by a GoverningBlood Glucose value BG_(gov). The Governing Blood Glucose valuesBG_(gov) in the process are selected based on the criteria of precedingthe previous occurrence of the dose to be adjusted by a sufficientamount of time for the effect (or lack of effect) of the insulin to beobservable and measurable in the value of the BG_(gov).

At block 802, the adjustment factor process 800 receives the GoverningGlucose value BG_(gov) from non-transitory memory 24, 114, 144, sincethe adjustment factor AF is determined using the Governing Glucose valueBG_(gov). To determine the adjustment factor AF, the adjustment factorprocess 800 considers the blood glucose target range BG_(TR) (withinwhich Basal doses and Meal Boluses, are not changed), which is definedby a lower limit, i.e., a low target BG_(TRL) and an upper limit, i.e.,a high target BG_(TRH). As previously discussed, the target rangeBG_(TR) is determined by a doctor 40 and entered manually (e.g., usingthe patient device 110 or the medical record system 140, via, forexample, a drop down menu list displayed on the display 116, 146). Eachtarget range BG_(TR) is associated with a set of configurable constantsincluding a first constant BG_(AFL), a second constant BG_(AFH1), and athird constant BG_(AFH2) shown in the below table.

TABLE 1 Target Range Settings Input Ranges BG_(AFL) BG_(TRL) BG_(TRH)BG_(AFH1) BG_(AFH2)  70-100 70 70 100 140 180  80-120 80 80 120 160 200100-140 70 100 140 180 220 120-160 90 120 160 200 240 140-180 110 140180 220 260

The adjustment factor process 800 determines, at block 804, if theGoverning Glucose value BG_(gov) is less than or equal to the firstconstant BG_(AFL) (BG_(gov)<=BG_(AFL)), if so then at block 806, theadjustment factor process 800 assigns the adjustment factor AF to afirst pre-configured adjustment factor AF1 shown in Table 2.

If, at block 804, the Governing Glucose value BG_(gov) is not less thanthe first constant BG_(AFL), (i.e., BG_(gov)≥BG_(AFL)), then at block808, the adjustment factor process 800 determines if the GoverningGlucose value BG_(gov) is greater than or equal to the first constantBG_(AFL) and less than the low target BG_(TRL) of the target rangeBG_(TR) (BG_(AFL)≤BG_(gov)<BG_(TRL)). If so, then the adjustment factorprocess 800 assigns the adjustment factor AF to a second pre-configuredadjustment factor AF2, at block 810. If not, then at block 812, theadjustment factor process 800 determines if the Governing Glucose valueBG_(gov) is greater than or equal to the low target BG_(TRL) of thetarget range BG_(TR) and less than the high target level BG_(TRH) of thetarget range BG_(TR) (BG_(TRL)≤BG_(gov)<BG_(TRH)). If so, then theadjustment factor process 800 assigns the adjustment factor AF to athird pre-configured adjustment factor AF3, at block 814. If not, thenat block 816, the adjustment factor process 800 determines if theGoverning Glucose value BG_(gov) is greater than or equal to the hightarget level BG_(TRH) of the target range BG_(TR) and less than thesecond constant BG_(AFH1) (BG_(TRH)≤BG_(gov)<BG_(AFH1)). If so, then theadjustment factor process 800 assigns the adjustment factor AF to afourth pre-configured adjustment factor AF4, at block 818. If not, thenat block 820, the adjustment process 800 determines if the GoverningGlucose value BG_(gov) is greater than or equal to the second constantBG_(AFH1) and less than the third constant BG_(AFH2)(BG_(AFH1)≤BG_(gov)<BG_(AFH2)). If so, then the adjustment factorprocess 800 assigns the adjustment factor AF to a fifth pre-configuredadjustment factor AF5, at block 822. If not, then at block 824, theadjustment process 800 determines that the Governing Glucose valueBG_(gov) is greater than or equal to the third constant BG_(AFH2)(BG_(gov)≥BG_(AFH2)); and the adjustment factor process 800 assigns theadjustment factor AF to a sixth pre-configured adjustment factor AF6, atblock 826. After assigning a value to AF the adjustment factor process800 returns the adjustment factor AF to the process requesting theadjustment factor AF at block 828 (e.g., the SubQ outpatient process1800 (FIG. 5A) (FIG. 5B)).

TABLE 2 Configurable values for Adjustment Factor AF AF1 = 0.8 AF2 = 0.9AF3 = 1 AF4 = 1.1 AF5 = 1.2 AF6 = 1.3

Referring to FIGS. 2A, 5A, and 5B, if the user 40 initiates asubcutaneous output patient process 1800 through selection of program200 at block 226, the subcutaneous outpatient process 1800 utilizes thepatient information 208 a and the patient SubQ information 216 a inputby the user 40 or the patient 10, as shown in FIGS. 2B-2F.

Basal insulin is for the fasting insulin-needs of a patient's body.Therefore, the best indicator of the effectiveness of the basal dose isthe value of the blood glucose BG after the patient 10 has fasted for aperiod of time. Meal Boluses are for the short-term needs of a patient'sbody following a carbohydrate-containing meal. Therefore, the bestindicator of the effectiveness of the Meal Bolus is a blood glucosemeasurement BG tested about one mean insulin-lifetime iLifeRapid afterthe Meal Bolus, where the lifetime is for the currently-used insulintype. For rapid-acting analog insulin the lifetime is convenientlysimilar to the time between meals.

FIG. 5A and FIG. 5B show the SubQ outpatient process 1800 a, 1800 b,respectively, for a patient 10 using patient portable devices includingthe glucometer 124 and the patient device 110 a or smartphone 110 b forcommunicating with, or optionally executing, the dosing controller 160,based upon selection of blocks 110 b and 124 of program 200 (FIG. 2A)The SubQ outpatient process 1800 may be similarly utilized for portabledevices including the insulin pen 123 b and the infusion pump 123 ahaving “smart” capabilities for communicating with the dosing controller160.

Referring to FIG. 5A the process 1800 a executes by a blood glucosemeter without a built-in correction dose calculator. The SubQ outpatientprocess 1800 a begins with a patient's 18 manual entry of a bloodglucose measurement BG at block 1802. The SubQ outpatient process 1800a, at block 1804, displays the result of the blood glucose measurement(e.g., 112 mg/dl) and prompts the patient 10 to select a BGtype from adropdown list 1806. The selection list is provided so that the patientcan choose the appropriate BGtype indicating which meal and whether theblood glucose measurement BG is “Before-Meal” or “After-Meal”, and alsolisting other key blood glucose testing times such as Bedtime andMidSleep (generally about 03:00 AM). The BGtype is associated with ablood glucose time BGtime associated with a time of measuring the bloodglucose measurement. In the example shown, the SubQ outpatient process1800 a allows the patient to select a pre-breakfast blood glucosemeasurement, a pre-lunch blood glucose measurement, a pre-dinner bloodglucose measurement, a bedtime blood glucose measurement, or a midsleepblood glucose measurement.

In some implementations, the glucometer 124 may not be configured todisplay the BGtype selections as shown at block 1806, and instead,determines if the time at which the blood glucose BG was measured BGtimefalls within one of a number of scheduled blood glucose time buckets,that are contiguous so as to cover the entire day with no gaps. Further,the BGtypes are provided with Ideal BG Time Intervals, where each idealscheduled time is associated with an interval configured with a starttime margin (M_(start)) and an end time margin (M_(End)). Moreover, eachinterval may be associated with a corresponding BGtype: pre-breakfastblood glucose measurement, a pre-lunch blood glucose measurement, apre-dinner blood glucose measurement, a bedtime blood glucosemeasurement, or a midsleep blood glucose measurement

Referring to FIG. 5B, the process 1800 b uses a blood glucose meterhaving a built-in correction dose calculator. Using a processor of theglucometer 124, the SubQ outpatient process 1800, at block 1810,determines a Correction Dose of insulin for the selected (or determined)BGtype (e.g., pre-breakfast) using the following equation (based on EQ.2):CB_(Breakfast)=(BG_(Breakfast)−BG_(Target))/CF  (11)

Additionally or alternatively, the Correction Dose may be determinedusing the Correction Dose Function of process 700 (FIG. 3). For example,when the blood glucose meter does not include a built-in correction dosecalculator, process 1800 a (FIG. 5A) may allow a healthcare provider,via the dosing controller 160, to load the Correction Factor (CF) uponthe glucometer 124 based upon an immediately preceding BG measurement.In other examples, meters or other devices may use the correction doseformula of process 700 (FIG. 3), which may incorporate a deduction forthe Remaining Insulin I_(Rem).

The SubQ outpatient process 1800 b (FIG. 5B), at block 1820, displaysthe Correction Dose for the BGtype determined at block 1810 on a MeterScreen of the glucometer display 116 c. In some implementations, theSubQ outpatient process 1800 a (FIG. 5A) stores blood glucose dataBGdata, including the recent correction dose CD, the blood glucosemeasurement BG, the BGtype, and the BG_(TIME), in the glucometer's 124memory 114 c at block 1840, and at a later time, the SubQ outpatientprocess 1800 uses a batch process, at blocks 1842-1846, for downloadingthe data from the glucometer 124 to the non-transitory 24, 114, 144 forthe dosing controller 160 to retrieve for determining or adjustingrecommended insulin doses for the patient 10. In some examples, theglucometer 124 transfers data to the computing device 112 or 142 atblock 1842, and a proprietary download program 196 provided by themanufacturer of the glucometer 124 executes on the computing device 112or 142 to download the data at block 1844. For instance, the patient 12may connect the glucometer 124 to the computing device 142 when thepatient 12 visits a clinic 42 during a regular check-up. The datatransfer may be facilitated by connecting the glucometer 124 to thecomputing device 112 or 142 using Bluetooth, Infrared, near fieldcommunication (NFC), USB, or serial cable, depending upon theconfiguration of the glucometer 124. The SubQ outpatient process 1800 a,at block 1846, exports the data downloaded by the proprietary downloadprogram 196 as a formatted data file for storage within thenon-transitory 24, 114, 144 for the dosing controller 160 to retrievewhen determining or adjusting insulin parameters for the patient 10 atentry point P. For example, the exported data file may be a CVS file orJSON file accessible to the computing devices 132, 142 of the dosingcontroller 160.

Referring back to block 1806, in some implementations, the SubQoutpatient process 1800 a, 1800 b provides the blood glucose BG data,including the recent correction dose CD, the blood glucose measurementBG, the BGtype, and the BG_(TIME), in real time to a web-basedapplication 198 of the manufacturer of the glucometer 124 at block 1814,and in turn, the web-based application 198 of the manufacturer via thenetwork 20 may format a data file of the BG data for storage in thenon-transitory memory 24, 114, 144 at block 1816. The glucometer 124 maysync the BG data with a mobile device, such as the smart phone 110 b, towirelessly transmit the BG data to the web-based application 198 atblock 1814. The computing devices 132, 142 of the dosing controller 160may retrieve the exported BD data file for calculating a nextrecommended insulin dose and a new value for the Correction Factor (CF)for the patient 10 at entry point Q. The next recommended insulin dosesfor adjusting the basal and the CF may be input to entry point Q using abasal adjustment process 2300 (FIG. 8), while recommended insulin dosesfor meal boluses may be input to entry point Q using a meal bolusadjustment process 2400 (FIG. 9). In some examples, the glucometer 124is configured to connect to the network 20 and transmit the bloodglucose data directly to the manufacturer's web-based application 198.In other examples, the glucometer 124 syncs with the smart phone orother mobile device 110 b to connect to the network 20 and transmit theblood glucose data to the manufacturer's web-based application 198. Insome examples, the glucometer 124 syncs with the smart insulin pump 123a or smart insulin pen 123 b to connect to the network 20 and transmitthe blood glucose data to the manufacturer's web-based application 198.The smart insulin pump 123 a or smart insulin pen 123 b includingadministration computing devices 112 d or 112 e configured tocommunicate the BG data to the dosing controller 160 and execute theSubQ outpatient program 200 transmitted from the dosing controller 160causing a doser 223 a, 223 b to administer recommended insulin dosesspecified by the SubQ outpatient program 200.

The SubQ outpatient process 1800 a, 1800 b transmits via the network 20the next recommended insulin dose and the new value for the CF for thepatient 10 calculated at 1816 to the web-based application 198 of themeter manufacturer at block 1818, wherein the web-based application 198of the meter manufacturer formats the next recommended insulin dose andthe new value for the CF for the glucometer 124 to receive via thenetwork 20. In some examples, the web-based application 198 transmitsthe next recommended dose and the new value for the CF to a mobiledevice, such as the smart phone 110 b, via the network 20 the mobiledevice 110 b syncs with the glucometer 124 (and/or smart pen 123 b) toprovide the next recommended dose and the new value for the CF to theglucometer 124 (and/or the smart pen 123 b). For instance, the number ofinsulin units associated with the recommended dose may be automaticallymeasured by the smart pen 123 b or smart pump 123 a. Next, the SubQoutpatient process 1800 displays the next recommended insulin dose forthe breakfast meal bolus on a Meter Screen via display 116 c at block1820.

After the patient self-administers the insulin dose (or the dosingcontroller 160 executing the SubQ outpatient process 1800 a, 1800 bcauses the doser 223 a, 223 b to administer the insulin dose), at block1824, the process 1800 a, 1800 b determines that the patient 10 hasselected a “Dose Entry” to record the administered dose. The SubQoutpatient Process 1800 a, 1800 b then prompts the patient 10 to selectthe insulin dose type on a Meter Screen via display 116 c at block 1826.The Meter Screen permits the patient to simultaneously select“Correction” and “Meal Bolus” for when the patient 10 has administered acombined dose that the patient 10 would like to record. The selection of“Basal” may not be selected simultaneously with another selection but isoperative to cancel out other selections. In response to the patient's10 selection, the SubQ outpatient process 1800 a, 1800 b, at block 1828,presents an insulin drop down menu of populated insulin doses on a MeterScreen via the display 116 c. Here, the patient 10 may select the numberof units of insulin recently administered by the patient 10.

In some implementations, as shown in FIG. 1C, when the patient 10 usesthe smart pen 123 b (or smart pump 123 a), the SubQ outpatient process1800 transmits via the network 20 the next recommended insulin dose andthe new value for the CF for the patient 10 calculated at entry point Qdirectly to the smart pen 123 b, wherein the smart pen 123 bautomatically dials in the recommended dose of insulin without manualinput by the patient 10 and may display the dose via the smart pen 123 bdisplay 116 e. In other implementations, the smart pen 123 b syncs(e.g., Bluetooth connection) with the glucometer 124 to receive andautomatically dial-in the recommended dose of insulin. In some examples,after the patient 10 administers the insulin dose, the smart pen 123 brecords the number of units of insulin administered by the patient whichmay be stored in the non-transitory memory 24, 114, 144 via the network20.

FIG. 6A shows a data flow process 1900 a for storing blood glucose BGdata from a patient's mobile device 110 a, 110 b, 124, 123 a, 123 bwithin the non-transitory memory 24, 134, 144 in communication with thecomputing device 112, 132, 142 of the dosing controller 160. The BG datamay include, but is not limited to, doses of insulin administered to thepatient 10, a blood glucose measurement BG, an associated BGtype, and anassociated time of the blood glucose measurement BGtime, as describedabove with reference to block 1806 of the SubQ outpatient process 1800a, 1800 b. In some implementations, the glucometer 124 syncs with thepatient's mobile device 110 a, 110 b, 124, 123 a, 123 b to transfer theBG data at block 1902. In the example shown, the mobile device is thesmart phone 110 b. The data flow process 1900 a permits the mobiledevice 110 b to transmit the BG data for storage in the non-transitorymemory 24, 134, 144 by using one of three data transfer paths.

In some implementations, the data flow process 1900 a sends the BG datain real-time via a first data transfer path from the mobile device 110 bat block 1902. The first data transfer path may always be availableprovided the mobile device 110 b is able to connect to the network 20 orcellular service. In some scenarios, the data flow process 1900 a, atblock 1902, sends the BG data in real-time via the first data transferpath from the mobile device 110 b to the computing device 192 of theservice provider 130. Thereafter, the data flow process 1900 a transmitsthe BG data from the first data transfer path, at block 1904, to amerged database within the non-transitory memory 24, 134, 144 at block1906.

In other implementations, the data flow process 1900 a executes a batchprocess for downloading the BG data from the memory 114 c of theglucometer 124 at the patient device 110 a or other computing deviceconnecting to the network 20 at block 1908, and then, transmits the BGdata from the patient device 110 a via a second data transfer path to aweb-based application 198 of the manufacturer of the glucometer 124 atblock 1910. In some examples, the batch process downloads all BG datastored on the memory 114 c of the glucometer 124 for a configurable timeperiod. In other examples, the batch process downloads all BG datastored on the memory 114 c of the glucometer 124 since an immediatelyprevious download session. The web-based application 198 may format adata file (e.g., merged database) of the BG data for storage in thenon-transitory memory 24, 114, 144 at block 1906.

In other implementations, the data flow process 1900 a executes a batchprocess for downloading the BG data from the memory 114 c of theglucometer 124 at the health care provider computing device 142 via athird data transfer path at block 1912. For instance, the patient 10 orhealth care professional 40 may connect the glucometer 124 to thecomputing device 142 when the patient 10 visits a clinic 42 associatedwith a hospital call center during a regular check-up. In some examples,the computing device 142 executes a proprietary download program 196provided by the manufacturer of the glucometer 124 to download the BGdata from the memory 114 c of the glucometer 124. The BG data transfermay be facilitated by connecting the glucometer 124 to the computingdevice 142 using Bluetooth, Infrared, near field communication (NFC),USB, or serial cable, depending upon the configuration of the glucometer124. In some examples, the BG data downloaded at block 1912 may bedisplayed via display 146 for the health care professional to view. Thedata flow process 1900 a receives a user 40 input to load the downloadedBG data (e.g., via a button on display 146), and exports the BG datadownloaded by the proprietary download program 196 as a formatted BGdata file for storage within the non-transitory 24, 114, 144 at block1916. For example, the exported BG data file may be a CVS file or JSONfile. In some examples, the batch process downloads all BG data storedon the memory 114 c of the glucometer 124 for a configurable timeperiod. In other examples, the batch process downloads all BG datastored on the memory 114 c of the glucometer 124 since an immediatelyprevious download session during a previous clinic visit by the patient10.

In some examples, the non-transitory memory 24, 114, 144 includes adatabase for storing the BG data of the patient 10 received from any oneof the first, second, or third data transfer paths. The database maystore the BG data in a designated file associated with the patient 10and identifiable with a patient identifier associated with the patient10. The BG data within the database of the non-transitory memory 24,114, 144 may be retrieved by the dosing controller 160 for determiningor adjusting doses of insulin for the patient 10 to administer. Block1906 may send the data within the merged database to Entry point T forrouting to other processes, including a Time Limits of Data forAdjustment process (FIG. 6B).

Moreover, block 1906 may provide the data within the merged database tothe patient's mobile device 110 a, 110 b, 124, 123 a, 123 b at block1902. For instance, block 1922 may determine if the mobile deviceincludes a self-sufficient application capable of sharing the mergeddatabase. If block 1922 is a “YES” indicating that the mobile deviceincludes the self-sufficient application, block 1920 provides the mergeddatabase to block 1924 for sharing with the mobile device. Thereafter,block 1924 may provide an adjusted basal dose (from process 2300 of FIG.8), an adjusted meal dose (from process 2400 of FIG. 9), a correctionfactor, and/or a carbohydrate-to-insulin ratio CIR (from process 2500 ofFIG. 10) over the network 20 directly to the mobile device via EntryPoint W at block 1926, or through the web-based application for themobile device via Entry Point Q at block 1928. If block 1922 is a “NO”indicating that the mobile device does not include a self-sufficientapplication, block 1924 may provide existing basal doses, meal doses,the correction factor, and/or the carbohydrate-to-insulin ratio over thenetwork 20 to the mobile device at block 1902 via one of block 1926 orblock 1928.

Referring to FIG. 6B, in some implementations, the Limits on Age of Datafor Adjustment process 1900 b receives the data of Entry Point T fromthe data flow process 1900 a of FIG. 6A. Additionally, process 1900 breceives, at block 1950, the configurable constants input at theHealthcare Facility Input Screen 2000 of FIG. 2G, including the constantMaxDays which sets a limit on the amount of data used based on thereasoning that a patient's health can change substantially over severalmonths. The currently configured number for MaxDays is 28 days. Block1952 shows the oldest allowable date/time (DateTimeOldLim) is midnight(00:00) on the day given by the current date less (minus) the MaxDays.The process 1900 b determines, at block 1954, the date/time of the lastadjustment (LastAdjustDateTime) from the patient's history from EntryPoint T. Thereafter, at block 1956, the process 1900 b determines thebeginning date/time for the current adjustment (DataStartDateTime) asthe most recent date/time between the DateTimeOldLim (block 1952) or theLastAdjustDateTime (block 1954). The process 1900 b may then provide theDataStartDateTime to block 1958 for routing to a Flag Corrector process1900 c (FIG. 6C) and to a Modal Day Scatter Chart upon the display 146(FIGS. 12A and 12B).

Blood glucose measurements may be aggregated or flagged according totheir associated blood glucose type BGtype or blood glucose time BG timeinterval to determine a mean or median blood glucose value (EQ. 3) foreach BGtype that may be used to determine or adjust recommended doses ofinsulin (e.g., bolus and/or basal). Referring to FIG. 6C, the FlagCorrector process 1900 c receives, at block 1960, the BG data from theprocess 1900 b (FIG. 6B). The glucometer 124 may include a selectablebutton to flag the BG measurements with a given BGtype (e.g.,pre-Breakfast, pre-Lunch, Bedtime, etc), as shown at the meter screen atblock 1804 of FIGS. 5A and 5B (e.g., glucometer display 116 d (FIG.1B)). In some scenarios, patients may infrequently flag BG measurementsor may flag the BG measurements incorrectly. In these scenarios, theprocess 1900 c executes a loop to examine all the BG measurements withina specified date range. Prior to executing the loop, the process 1900 c,at block 1962, initializes variables for the loop to examine all theblood glucose BG measurements in a date range. The initialized variablesmay be re-usable dummy variables. Thereafter, the loop starts at block1964 by retrieving each BG measurement moving backward in time. Block1966 determines whether the date/time of the analyzed BG measurement islater than the DataStartDateTime. If block 1966 determines that thedate/time of the BG measurement is not later than the DataStartDateTime(e.g., block 1966 is “NO”), then the loop stops at block 1967. Here, allthe BG measurements in the date-range have now been checked andincorrect flags have been corrected; however, the last BG measurementchecked/analyzed was not in the date-range and is therefore excludedfrom routing to Entry Point V. The process 1900 c routes the correcteddata through entry point V, whereby the analyzed BG measurements areselected and provided to either the Typical Non-Meal Bucket process 2200a (FIG. 7A) or the Typical Meal Bucket process 2200 b (FIG. 7D). If, onthe other hand, block 1966 determines that the date/time of the BGmeasurement is not later than the DataStartDateTime (e.g., block 1966 is“YES”), then the analyzed BG measurement is checked at block 1968 todetermine whether the BG measurement is outside of the bucket for whichit is flagged. For instance, if the time of a BG measurement is outsideof a bucket indicated by an associated flag by more than a configurablemargin (FlagMargin), then the loop changes the flag to reflect theBGtype indicated by the actual time of the BG measurement. The process1900 c then reverts back to block 1964 and retrieves the next earlier BGmeasurement in time. The process 1900 c ends executing the loop whenblock 1970 determines a BG is found earlier than the DataStartDateTime,and all the data in the acceptable date-range is provided to Entry PointV for routing to a BG aggregation process 2200 (FIGS. 7A-7F).

If the time of a BG is outside of the bucket indicated by its flag bymore than a configurable margin (FlagMargin) then the flag is changed toreflect the BGtype indicated by the actual time of the BG. The loop usessome dummy variables that are re-used, so they are initialized at thestart at 2904. The start of the loop at 2906 starts at the present andretrieves each BG moving backward in time. If the date/time of the BGbeing checked at 2908 is earlier than the DataStartDateTime, then theloop is stopped, if not then the time of the BG is checked at 2912 tosee if it is outside the bucket for which it is flagged. If so then theflag is changed at 2914 to indicate the bucket actually inhabited by theBG. The loop ends when a BG is found earlier that theDataStarteDateTime, and all the data in the acceptable date-time rangeare sent to Entry Point V for use by the BG aggregation processes 2200a, 2200 b.

FIGS. 7A-7F show the blood glucose BG aggregation process 2200 foraggregating blood glucose BG measurements for a patient 10 according tothe times at which the blood glucose measurements are measured. Theaggregation process 2200 a, 2200 of FIGS. 7A-7C aggregates BGmeasurements that are not associated with times when the patient 10 isnot consuming meals, while the aggregation process 2200, 2200 b of FIGS.7D-7F aggregates BG measurements associated with times when the patient10 is consuming meals.

In some examples, the BG measurements are transmitted from the patient's10 portable device 110 a, 110 b, 124, 123 a, 123 b and stored within thenon-transitory memory 24, 134, 144. For instance, the BG measurementsobtained by the glucometer 124 may be communicated and stored within thenon-transitory memory 24, 134, 144 by using the data flow process 1900a, as described above with reference to FIG. 6A. In someimplementations, the BG aggregation process 2200 divides a day into fivetime intervals corresponding to the five BG types: Midsleep, Breakfast,Lunch, Dinner, and Bedtime. As used herein, the term “time buckets” isused to refer to these time intervals corresponding to the five BGtypes. The Modal Day Scatter Chart 502 of FIG. 12B shows the timebuckets as intervals between the dotted lines. Each bucket is associatedwith a corresponding time boundary that does not overlap the other timeboundaries associated with the other buckets.

Referring to FIG. 2H, in some examples, a BG Time-Bucket input screenpermits the user 40 (or patient 10) to adjust the time-boundaryassociated with each time bucket via the display 116, 146. The BGTime-Bucket input screen displays the patient information 208 a andallows the user 40 to input BG Time-Bucket Information 260 and IdealMealtime information 262. For instance, the BG Time-Bucket Information260 includes a bucket name (e.g., MidSleep, Breakfast, Lunch, Dinner,Bedtime) and associated start and end times for each BG time-bucket.Based upon the BG Time-Bucket Information 260 and the Ideal Mealtimeinformation 262 input to the BG Time-Bucket input screen (FIG. 2H), theBG aggregation process 2200 a (FIGS. 7A-7C) may associate the BGtime-buckets for MidSleep and Bedtime with time intervals when thepatient 10 does not consume meals and the BG aggregation process 2200 b(FIGS. 7D-7F) may associate the BG time-buckets for Breakfast, Lunch andDinner with time intervals when the patient 10 consumes meals.

Referring back to FIG. 12B, the Modal Day Scatter Chart 502 applies aDayBucket to an interval of time within a time-bucket on a specific day.Thus, each time-bucket may include one or more DayBuckets. The user 40may select an Aggregation Method (AgMeth) for use within each of theDayBuckets from an Aggregation Menu 510 upon the Modal Day Scatter Chartvia the display 146. For example, the user 40 may select an AgMeth fromthe Aggregation Menu 510 that includes one of Minimum Earliest, Mean, orMedian for the BG measurements in the associated DayBucket. Accordingly,the AgMeth selected by the user results in a single value representingthe BG measurements associated with the DayBucket. The BG measurementsaggregated by the AgMeth may belong to a union of 1 or more subsetsdenoted by the symbol “U”. These values are further aggregated for eachBG Bucket over the days in the updated data. The Modal Day Scatter Chart502 of FIG. 12B shows the aggregation methods available for thisaggregation are mean and median and are governed by the variable(MMtype).

Referring to FIG. 7A, the BG aggregation process 2200 a aggregates theBG measurements of the BG time-buckets (e.g., MidSleep and Bedtime) fortime intervals when the patient 10 does not consume meals. While FIG. 7Ashows the BG aggregation process 2200 a aggregating BG measurements forthe Bedtime BG time-bucket, the BG aggregation process 2200 a similarlyaggregates BG measurements for the Midsleep BG time-bucket. Theaggregation process 2200 a provides the DataStartDataTime (FIG. 6) viaEntry Point V to block 2202 for determining a NdaysBedtime (orNdaysMidSleep) that counts the number of DayBuckets within theassociated bucket (e.g., Bedtime BG time-bucket) from the currentdate/time backward to an earliest permissible date/timeDataStartDateTime. As used herein, the “earliest date” refers to theearliest one of a previous dosing adjustment or the preconfiguredMaxDays (FIG. 6B) into the past. The “earliest date” is operative as asafeguard against a patient returning to the healthcare facility after ayear, and receiving a subsequent 365 day adjustment. Additionally, theaggregation process 2200 a determines, at block 2202, a NDayBucketsWBGthat counts the number of the DayBuckets containing at least one BGmeasurement.

At block 2204, the aggregation process 2200 a determines a ratio of theDayBuckets containing BG measurements to DayBuckets in the associatedbucket (e.g., NDayBucketsWBG/NdaysBedtime) and compares the ratio to aconfigurable set point (Kndays). The value of Kndays is presentlyconfigured at 0.5. If the ratio is less than Kndays, the aggregationprocess 2200 a prevents, at block 2206, the dosing controller 160 fromadjusting the dose governed by the associated time-bucket (e.g., BedtimeBG time-bucket). For example, when the aggregation process 2200 aaggregates BG measurements for the Bedtime BG time-bucket, block 2206prevents the adjustment of the Dinner meal bolus when the ratio ofNDayBucketsWBG/NdaysBedtime is less than Kndays indicating that theBedtime BG time-bucket does not contain enough BG measurements. Block2206 provides the determination that prevents adjusting the dosegoverned by the associated time-bucket to Entry Point S for use byprocesses 2300, 2400, 2500 of FIGS. 8, 9, and 10, respectively. On theother hand, if block 2204 determines that the ratio ofNDayBucketsWBG/NdaysBedtime is greater than or equal to Kndays, thedosing controller 160 is permitted to adjust the dose governed by theassociated time-bucket.

The aggregation process 2200 a of FIG. 7A and the aggregation process2200 b of FIG. 7B use a system of filters to determine the bestaggregate BG value to represent the associated time-bucket. There aretwo dropdown filter selections (Filter1 512 and Filter2 514) that theuser 40 may select from the Modal Day Scatter Chart 502 of FIG. 12B.Each of the dropdown filter selections 512, 514 allow the user 40 toselect from the following selections:

Flags: Uses the flags entered by the patient 10 on the glucometer 124 attest time and corrected as needed by the Flag Corrector Process 1900 c(FIG. 6C).

Pre-Meal Bolus: Uses BG Measurements within the bucket that occurearlier than the time of the Meal Bolus (not available for non-mealbuckets).

Ideal Meal Time: Shaded areas of the Modal Day Scatter Chart 502 (FIG.12B) within each associated bucket. Each Ideal Meal Time havingboundaries adjustable using drag-and-drop methods by user inputs uponthe Modal Day Scatter Chart (FIG. 12B) or via inputs to the IdealMealtime information 262 at the BG-time Buckets Input Screen (FIG. 2H).

Both Pre-Meal-bolus OR Ideal Mealtimes: Uses the union of the sets of BGMeasurements associated with both the Pre-Meal Bolus and the Ideal MealTime filters.

All: Uses all the BG measurements within the associated bucket.

None: does not apply a filter.

Referring back to FIG. 7A, the aggregation process 2200 a for thenon-meal BG time-buckets (e.g., MidSleep and Bedtime) executes a loop atblock 2208 when block 2204 determines that the ratio ofNDayBucketsWBG/NdaysBedtime is greater than or equal to Kndays.Specifically, the aggregation process 2200 a examines, at block 2208,all the DayBuckets in the associated time-bucket (e.g., Bedtime BGtime-bucket) back to the DataStartDateTime based on the filterselections 512, 514 of the Modal Day Scatter Chart 502 (FIG. 12B)received via block 2230. At block 2210, the aggregation process 2200 aexamines whether or not Filter1 512 includes “Flags”. If the Filter1 512includes “Flags” (e.g., block 2210 is “YES”), the aggregation process2200 a proceeds to block 2212 for executing subroutine process 2260 a(FIG. 7B). On the other hand, if the Filter1 512 does not include“Flags” (e.g., block 2210 is “NO”), the aggregation process 2200 aproceeds to block 2214 for executing subroutine process 2280 a (FIG.7C). The two subroutine processes 2260 a, 2280 a aggregate the BGmeasurements to a single BG value in each associated DayBucket or noneif the associated DayBuckets are empty. The outputs determined by thetwo subroutine processes 2260 a, 2280 a are provided back to theaggregation process 2200 a (FIG. 7A), and at block 2216, the aggregationprocess 2200 a determines a running sum BGsum of the filtered BGmeasurements. At block 2218, the loop ends and the aggregation process2200 a determines, at block 2220, a mean of the filtered BG measurementsBGmean as the sum of the filtered BG measurements (BGsum) divided by thenumber of DayBuckets with at least one BG inside, (NdayBucketsWBG). Inother configurations, the BGmean may be determined by other methods.

The parameter MMtype is associated with a “mean or median type” thatcontrols a choice of the aggregation method applied to the results ofthe DayBucket aggregations, i.e. mean or median. The Modal Day ScatterChart 502 (FIG. 12B) may include a selector for choosing the MMtypeinput to block 2222 for routing to block 2224 of the aggregation process2200 a. At block 2224, the aggregation process 2200 a determines if theNDayBucketsWBG (e.g., the number of filtered BG measurements within theassociated time-bucket) is greater than a minimum number of BGmeasurements required for determining a median value (NLimMedian). Ifthe NDayBucketsWBG is greater than the NLimMedian or if the user 40manually selects “median” as the MMtype (e.g., block 2224 is “YES”),then the aggregation process 2200 a proceeds to block 2226 forcalculating the BGbedtime using the median value of NDayBucketsWBGwithin the time-bucket associated with the Bedtime BG time-bucket. If,however, the NDayBucketsWBG is equal to or less than the NLimMedian(e.g., block 2224 is “NO”), then the aggregation process 2200 a proceedsto block 2228 for calculating the BGbedtime using the mean value(BGmean) of NDayBucketsWBG within the time-bucket associated with theBedtime BG time-bucket. Thereafter, the aggregation process 2200 aroutes the BGbedtime value (or BGMidsleep value) calculated using themedian (block 2226) or the BGmean (block 2228) to Entry Point G for useby processes 2300, 2400, 2500 of FIGS. 8, 9, and 10, respectively.

Referring to FIG. 7B, the subroutine process 2260 a executes when theaggregation process 2200 a (FIG. 7A) determines that the Filter1 512includes “Flags” (e.g., block 2210 is “YES”). At block 2262 a, thesubroutine process 2260 a provides the determination that Filter1 512includes “Flags” from block 2212 of the aggregation process (FIG. 7A) toblock 2264 a, and block 2264 a determines whether or not a filter2 514applies a filter for the associated time-bucket (e.g., Bedtime BGtime-bucket). If filter2 514 is not applying any filters to the BedtimeBG time-bucket (e.g., block 2264 a is “YES”), then the subroutineprocess 2260 a sets the BG value in the nth DayBucket, BGbedtimeDB(n)equal to the selected aggregate method AgMeth, at block 2266 a to all BGmeasurements flagged “bedtime” in the DayBucket. The subroutine process2260 a routes BGbedtimeDB(n) back to block 2212 of the aggregationprocess 2200 a (FIG. 7A), where each BG measurement representing aDayBucket “n” BGbedtimeDB(n) within the aggregation process 2200 a loopis added to a running sum at block 2216 in preparation for calculatingthe mean.

If, however, block 2264 a determines that filter2 514 is applying afilter to the Bedtime BG time-bucket (e.g., block 2264 a is “NO”), thenthe subroutine process 2260 a determines, at block 2268 a, whether theselected filter applied by filter2 514 includes the “Ideal Mealtimes”filter. If filter 2 514 is applying the “Ideal Mealtimes” filter (e.g.,block 2268 a is “YES”), then the subroutine process 2260 a sets the BGvalue in the nth DayBucket, BGbedtimeDB(n) equal to the selectedaggregate method AgMeth applied, at block 2270 a to the union of all BGmeasurements flagged “bedtime” in the DayBucket together with allnon-flagged BG measurements within the Ideal Mealtimes filter.Thereafter, the subroutine process 2260 a routes BGbedtimeDB(n) back toblock 2212 of the aggregation process 2200 a (FIG. 7A), whereby each BGmeasurement representing a BGbedtimeDB(n) within the aggregation process2200 a loop is added to a running sum at block 2216 in preparation forcalculating the mean.

On the other hand, if filter2 514 is not applying the “Ideal Mealtimes”filter (e.g., block 2268 a is “NO”), then the subroutine process 2260 adetermines, at block 2272 a, whether the selected filter applied byfilter2 514 includes the “All” filter corresponding to the use of all BGmeasurements within the associated time-bucket (e.g., Bedtime BGtime-bucket). When filter2 514 includes the “All” filter (e.g., block2272 a is “YES”), the subroutine process 2260 a sets the BG value in thenth DayBucket, BGbedtimeDB(n) equal to the selected aggregate methodAgMeth applied at block 2274 a to the union of all BG measurementsflagged “bedtime” in the DayBucket together with all non-flagged BGmeasurements within the entire Bedtime DayBucket. Thereafter, thesubroutine process 2260 a routes the BGbedtimeDB(n) back to block 2212of the aggregation process 2200 a (FIG. 7A), whereby each BGmeasurement(s) representing the BGbedtimeDB(n) within the aggregationprocess 2200 a loop is is added to a running sum at block 2216 inpreparation for calculating the mean. The value of BGbedtimeDB(n) routedback to Block 2212 of the aggregation process 2200 a from one of blocks2270 a, 2274 a fills the nth iteration of the loop. If, however, filter2514 does not include the “All” filter (e.g., block 2272 a is “NO”), thenthe aggregation process 2200 a proceeds to block 2276 a and postsmessage: “Check filter settings” upon the display 116, 146.

Referring to FIG. 7C, the subroutine process 2280 a executes when theaggregation process 2200 a (FIG. 7A) determines that the Filter1 512does not include “Flags” (e.g., block 2210 is “NO”). At block 2282 a,the subroutine process 2280 a provides the determination that Filter1512 does not include “Flags” from block 2214 of the aggregation process(FIG. 7A) to block 2284 a, and block 2284 a determines whether or notthe selected filter applied by filter2 514 includes the “IdealMealtimes” filter. If filter2 514 is applying the “Ideal Mealtimes”filter (e.g., block 2284 a is “YES”), then the subroutine process 2280 asets, at block 2286 a, the BG value in the nth DayBucket, BGbedtimeDB(n)equal to the selected aggregate method AgMeth applied to all non-flaggedBG measurements within the time interval filtered by the IdealMealtimes. Thereafter, the subroutine process 2280 a routesBGbedtimeDB(n) back to block 2214 of the aggregation process 2200 a(FIG. 7A), where each BG measurement representing BGbedtimeDB(n) withinthe aggregation process 2200 a loop is added to a running sum at block2216 in preparation for calculating the mean.

On the other hand, if filter2 514 is not applying the “Ideal Mealtimes”filter (e.g., block 2284 a is “NO”), then the subroutine process 2280 adetermines, at block 2288 a, whether the selected filter applied byfilter2 514 includes the “All” filter corresponding to the use of all BGmeasurements within the associated time-bucket (e.g., Bedtime BGtime-bucket). If filter2 514 is applying the “All” filter (e.g., block2288 a is “YES”), then the subroutine process 2280 a sets, at block 2290a, the BG value in the nth DayBucket, BGbedtimeDB(n) equal to theselected aggregate method AgMeth applied to all non-flagged BGmeasurements within the “bedtime” DayBucket. Thereafter, the subroutineprocess 2280 a routes the BGbedtimeDB(n) back to block 2214 of theaggregation process 2200 a (FIG. 7A), where each BG measurement(s)representing BGbedtimeDB(n) within the aggregation process 2200 a loopis added to a running sum at block 2216 in preparation for calculatingthe mean. The value routed back to Block 2214 of the aggregation process2200 a from one of blocks 2286 a, 2290 a fills the nth iteration of theloop. If, however, the filter2 514 is not applying the “All” filter(e.g., block 2288 a is “NO”), then at block 2292 a, the subroutineprocess 2280 a, posts message: “Check filter settings” upon the display116, 146.

Referring to FIG. 7D, the BG aggregation process 2200 b aggregates theBG measurements of the BG time-buckets (e.g., Breakfast, Lunch, andDinner) for time intervals when the patient 10 consumes meals. WhileFIG. 7D shows the BG aggregation process 2200 b aggregating BGmeasurements for the Breakfast time-bucket, the BG aggregation process2200 a similarly aggregates BG measurements for the Lunch and Dinner BGtime-buckets. The aggregation process 2200 b provides theDataStartDataTime (FIG. 6) via Entry Point V to block 2232 fordetermining a NdaysBreakfast (or NdaysLunch or NdaysDinner) that countsthe number of DayBuckets within the associated bucket (e.g., BreakfastBG time-bucket) from the current date/time backward to an earliestpermissible date/time DataStartDateTime. Additionally, the aggregationprocess 2200 b determines, at block 2232, an NDayBucketsWBG that countsthe number of the DayBuckets containing at least one BG measurement.

At block 2234, the aggregation process 2200 b determines a ratio of theDayBuckets containing BG measurements to DayBuckets in the associatedbucket (e.g., NDayBucketsWBG/NdaysBreakfast) and compares the ratio to aconfigurable set point (Kndays). The value of Kndays is presentlyconfigured at 0.5. If the ratio is less than Kndays, the aggregationprocess 2200 b prevents, at block 2236, the dosing controller 160 fromadjusting the dose governed by the associated time-bucket (e.g.,Breakfast BG time-bucket). For example, when the aggregation process2200 b aggregates BG measurements for the Breakfast BG time-bucket,block 2236 prevents the adjustment of the basal dose when the ratio ofNDayBucketsWBG/NdaysBreakfast is less than Kndays indicating that theBreakfast BG time-bucket does not contain enough BG measurements. Withrespect to the Lunch BG time-bucket, block 2236 would prevent theadjustment of the Breakfast meal bolus when the ratio ofNDayBucketsWBG/NdaysLunch is less than Kndays. Similarly, when the ratioof NDayBucketsWBG/NdaysDinner is less than Kndays, block 2236 wouldprevent the adjustment of the Lunch meal bolus. Block 2236 provides thedetermination that prevents adjusting the dose governed by theassociated time-bucket to Entry Point S for use by processes 2300, 2400,2500 of FIGS. 8, 9, and 10, respectively. On the other hand, if block2234 determines that the ratio of NDayBucketsWBG/NdaysBreakfast isgreater than or equal to Kndays, the dosing controller 160 is permittedto adjust the dose governed by the associated time-bucket.

The aggregation process 2200 b for the meal BG time-buckets (e.g.,Breakfast, Lunch, and Dinner) executes a loop at block 2238 when block2234 determines that the ratio of NDayBucketsWBG/NdaysBreakfast isgreater than or equal to Kndays. Specifically, the aggregation process2200 b examines, at block 2238, all the DayBuckets in the associatedtime-bucket (e.g., Breakfast BG time-bucket) back to theDataStartDateTime based on the filter selections 512, 514 of the ModalDay Scatter Chart (FIG. 12B) received via block 2259. At block 2240, theaggregation process 2200 b examines whether or not Filter1 512 includes“Flags”. If the Filter1 512 includes “Flags” (e.g., block 2240 is“YES”), the aggregation process 2200 b proceeds to block 2242 forexecuting subroutine process 2260 b (FIG. 7E). On the other hand, if theFilter1 512 does not include “Flags” (e.g., block 2240 is “NO”), theaggregation process 2200 b proceeds to block 2244 for executingsubroutine process 2280 b (FIG. 7F). The two subroutine processes 2260b, 2280 b aggregate the BG measurements to a single BG value in eachassociated DayBucket or none if the associated DayBuckets are empty. Theoutputs determined by the two subroutine processes 2260 b, 2280 b areprovided back to the aggregation process 2200 b (FIG. 7D), and at block2246, the aggregation process 2200 b determines a running sum BGsum ofthe filtered BG measurements. At block 2248, the loop ends and theaggregation process 2200 a determines, at block 2250, a mean of thefiltered BG measurements BGmean as the sum of the filtered BGmeasurements (BGsum) divided by the number of DayBuckets with at leastone BG (NdayBucketsWBG). In other configurations, the BGmean may bedetermined by other methods.

As set forth above in the aggregation process 2200 a (FIG. 7A), theparameter MMtype is associated with a “mean or median type” thatcontrols the choice of the aggregation method applied to the results ofthe DayBucket aggregations, i.e. mean or median. Here, the selector ofthe Modal Day Scatter Chart 502 (FIG. 12B) chooses the MMtype input toblock 2252 for routing to block 2254 of the aggregation process 2200 b.At block 2254, the aggregation process 2200 b determines if theNDayBucketsWBG (e.g., the number of filtered BG measurements within theassociated time-bucket) is greater than a minimum number of BGmeasurements required for determining a median value (NLimMedian). Ifthe NDayBucketsWBG is greater than the NLimMedian or if the user 40manually selects “median” as the MMtype (e.g., block 2254 is “YES”),then the aggregation process 2200 b proceeds to block 2256 forcalculating the BGBreakfast using the median value of NDayBucketsWBGwithin the time-bucket associated with the Breakfast BG time-bucket. If,however, the NDayBucketsWBG is equal to or less than the NLimMedian(e.g., block 2254 is “NO”), then the aggregation process 2200 b proceedsto block 2258 for calculating the BGBreakfast using the mean value(BGmean) of NDayBucketsWBG within the time-bucket associated with theBreakfast BG time-bucket. Thereafter, the aggregation process 2200 broutes the BGBreakfast value (or BGLunch or BGDinner values) calculatedusing the median (block 2256) or the BGmean (block 2258) to Entry PointH for use by processes 2300, 2400, 2500 of FIGS. 8, 9, and 10,respectively.

Referring to FIG. 7E, the subroutine process 2260 b executes when theaggregation process 2200 b (FIG. 7D) determines that the Filter1 512includes “Flags” (e.g., block 2240 is “YES”). At block 2262 b, thesubroutine process 2260 b provides the determination that Filter1 512includes “Flags” from block 2242 of the aggregation process 2200 b (FIG.7D) to block 2264 b, and block 2264 b determines whether or not afilter2 514 applies a filter for the associated time-bucket (e.g.,Breakfast BG time-bucket). If filter2 514 is not applying any filters tothe Breakfast BG time-bucket (e.g., block 2264 b is “YES”), then thesubroutine process 2260 b at block 2266 b, sets the aggregate value ofthe BG's in the nth DayBucket of the Breakfast bucket, BGBreakfastDB(n)to the selected aggregate method AgMeth applied to all BG measurementsflagged “Breakfast” in the DayBucket. The subroutine process 2260 broutes BGBreakfastDB(n) back to block 2242 of the aggregation process2200 b (FIG. 7D), where each BG measurement representing DayBucket “n”,BGBreakfastDB(n) within the aggregation process 2200 b loop is added atblock 2246 to a running sum in preparation for calculating a mean.

If, however, block 2264 b determines that filter2 514 is applying afilter to the Breakfast BG time-bucket (e.g., block 2264 b is “NO”),then the subroutine process 2260 b determines, at block 2268 b, whetherthe selected filter applied by filter 2 514 includes the Pre-Meal Bolus“PreMealBol” filter. If filter 2 514 is applying the “Pre-Meal Bolus”filter (e.g., block 2268 b is “YES”), then the subroutine process 2260 aat block 2270 b, sets the aggregate value of the BG's in the nthDayBucket of the Breakfast bucket, BGBreakfastDB(n) to the selectedaggregate method AgMeth applied to the union of the set of BGmeasurements flagged “breakfast” in the DayBucket together with the setof all non-flagged BG measurements having times earlier than a time ofthe breakfast meal bolus (TimeMealBolus). Thereafter, the subroutineprocess 2260 b routes BGBreakfastDB(n) back to block 2242 of theaggregation process 2200 b (FIG. 7D), where each BG measurementrepresenting BGBreakfastDB(n) within the aggregation process 2200 b loopis added at block 2246 to a running sum in preparation for calculationof a mean. When the subroutine process 2260 b determines, at block 2268b, that filter2 514 is not applying the Pre-Meal Bolus filter (e.g.,block 2268 b is “NO”), the subroutine process 2260 b proceeds to block2272 b.

At block 2272 b, the subroutine process 2260 b determines whether theselected filter applied by filter2 514 includes the “Ideal Mealtimes”filter. If filter2 514 is applying the “Ideal Mealtimes” filter (e.g.,block 2272 b is “YES”), then the subroutine process 2260 b at block 2274b, sets BGBreakfastDB(n) to the selected aggregate method AgMeth appliedto the union of the set of BG measurements flagged “breakfast” in theDayBucket together with the set of non-flagged BG measurements withinthe Ideal Mealtimes filter for breakfast. Thereafter, the subroutineprocess 2260 b routes BGBreakfastDB(n) back to block 2242 of theaggregation process 2200 b (FIG. 7D), where each BG measurementrepresenting BGBreakfastDB(n) within the aggregation process 2200 b loopis added at block 2246 to a running sum in preparation for calculationof a mean.

On the other hand, if filter2 514 is not applying the “Ideal Mealtimes”filter (e.g., block 2272 b is “NO”), then the subroutine process 2260 bdetermines, at block 2275 b, whether the selected filter applied byfilter2 514 includes the “Pre-MealBolus OR IdealMealtime” filter, whichpasses a union of the sets of BG's that meet the Pre-Meal Bolus filtercriteria or Ideal Mealtimes filter criteria. If filter2 514 is applyingthe “Pre-MealBolus OR IdealMealtime” filter (e.g., block 2275 b is“YES”), then the subroutine process 2260 b, at block 2276 b, setsBGBreakfastDB(n) to the selected aggregate method AgMeth applied to theunion of the set of BG measurements flagged “breakfast” in the DayBuckettogether with the set of all non-flagged BG measurements having timesearlier than TimeMealBolus for breakfast together with the set ofnon-flagged BG measurements within the Ideal Mealtime interval forbreakfast. Thereafter, the subroutine process 2260 b routesBGBreakfastDB(n) back to block 2242 of the aggregation process 2200 b(FIG. 7D), where each BG measurement representing BGBreakfastDB(n)within the aggregation process 2200 b loop is added at block 2246 to arunning sum in preparation for calculation of a mean. When thesubroutine process 2260 b determines, at block 2275 b, that filter2 514is not applying the “Pre-MealBolus OR IdealMealtime” filter (e.g., block2275 b is “NO”), the subroutine process 2260 b proceeds to block 2277 b.

At block 2277 b, the subroutine process 2260 b determines whether theselected filter applied by filter2 514 includes the “All” filtercorresponding to the use of all BG measurements within the associatedtime-bucket (e.g., Breakfast BG time-bucket). At block 2278 b, thesubroutine process 2260 b sets BGBreakfastDB(n) to the the selectedaggregate method AGMeth applied to the union of the set of BGmeasurements flagged “breakfast” in the DayBucket together with the setof all non-flagged BG measurements within the entire BreakfastDaybucket. Thereafter, the subroutine process 2260 b routesBGBreakfastDB(n) back to block 2242 of the aggregation process 2200 b(FIG. 7D), where each BG measurement representing the BGBreakfastDB(n)within the aggregation process 2200 b loop is added at block 2246 to arunning sum in preparation to calculation of a mean. The value routedback to Block 2242 of the aggregation process 2200 b from one of blocks2266 b, 2270 b, 2274 b, 2276 b, 2278 b fills the nth iteration of theloop. If, however, the filter2 514 is not applying the “All” filter(e.g., block 2277 b is “NO”), then at block 2279 b, the subroutineprocess 2260 b, posts message: “Check filter settings” upon the display116, 146.

Referring to FIG. 7F, the subroutine process 2280 b executes when theaggregation process 2200 b (FIG. 7D) determines that the Filter1 512does not include “Flags” (e.g., block 2240 is “NO”). At block 2282 b,the subroutine process 2280 b provides the determination that Filter1512 does not include “Flags” from block 2244 of the aggregation process2200 b (FIG. 7D) to block 2284 b, and block 2284 b determines whether ornot the selected filter applied by filter2 514 includes the “Pre-MealBolus” filter. If filter 2 514 is applying the “Pre Meal Bolus” filter(e.g., block 2284 b is “YES”), then the subroutine process 2280 b, atblock 2286 b, sets BGBreakfastDB(n) to the selected aggregate methodAgMeth applied to all BG measurements having times earlier than the timeof the associated breakfast meal bolus (TimeMealBolus). Thereafter, thesubroutine process 2280 b routes BGBreakfastDB(n) back to block 2244 ofthe aggregation process 2200 b (FIG. 7D), where each BG measurementrepresenting BGBreakfastDB(n) within the aggregation process 2200 b loopis added at block 2246 to a running sum in preparation for calculating amean. When the subroutine process 2260 b determines, at block 2284 b,that filter2 514 is not applying the Pre Meal Bolus filter (e.g., block2284 b is “NO”), the subroutine process 2280 b proceeds to block 2288 b.

At block 2288 b, the subroutine process 2280 b determines whether theselected filter applied by filter2 514 includes the “Ideal Mealtimes”filter. If filter2 514 is applying the “Ideal Mealtimes” filter (e.g.,block 2288 b is “YES”), then the subroutine process 2280 b, at block2290 b, sets BGBreakfastDB(n) to the selected aggregate method AgMethapplied to all BG measurements within the Ideal Mealtimes interval(e.g., ideal time filter) for breakfast. Thereafter, the subroutineprocess 2280 b routes BGBreakfastDB(n) back to block 2244 of theaggregation process 2200 b (FIG. 7D), where each BG measurement(s)representing BGBreakfastDB(n) within the aggregation process 2200 b loopis added at block 2246 to a running sum in preparation for calculating amean.

On the other hand, if filter2 514 is not applying the “Ideal Mealtimes”filter (e.g., block 2288 b is “NO”), then the subroutine process 2280 bdetermines, at block 2292 b, whether the selected filter applied byfilter2 514 includes the “Pre-MealBolus OR Ideal Mealtimes” filter,which passes the BG's that pass either the Pre Meal Bolus filter or theIdeal Mealtimes filter. If filter2 514 is applying the “Both” filter(e.g., block 2292 b is “YES”), then the subroutine process 2280 b, atblock 2294 b, sets BGBreakfastDB(n) to the selected aggregate methodAgMeth applied to the union of the set of all BG measurements havingtimes earlier than TimeMealBolus for breakfast together with the set ofall BG measurements within the Ideal Mealtime interval for breakfast.Thereafter, the subroutine process 2280 b routes BGBreakfastDB(n) backto block 2244 of the aggregation process 2200 b (FIG. 7D), where each BGmeasurement representing BGBreakfastDB(n) within the aggregation process2200 b loop is added at block 2246 to a running sum in preparation forcalculating a mean. When the subroutine process 2280 b determines, atblock 2292 b, that filter2 514 is not applying the “Both” filter (e.g.,block 2292 b is “NO”), the subroutine process 2280 b proceeds to block2296 b.

At block 2296 b, the subroutine process 2280 b determines whether theselected filter applied by filter2 514 includes the “All” filtercorresponding to the use of all BG measurements within the associatedtime-bucket (e.g., Breakfast BG time-bucket). At block 2298 b, thesubroutine process 2280 b sets BGBreakfastDB(n) to the selectedaggregate method AGMeth applied to all BG measurements within the entireBreakfast DayBucket. Thereafter, the subroutine process 2280 b routesBGBreakfastDB(n) back to block 2244 of the aggregation process 2200 b(FIG. 7D), where each BG measurement representing the BGBreakfastDB(n)within the aggregation process 2200 b loop is added at block 2246 to arunning sum in preparation for calculating a mean. The value routed backto Block 2244 of the aggregation process 2200 b from one of blocks 2286b, 2290 b, 2294 b, 2298 b fills the nth iteration of the loop. If,however, the filter2 514 is not applying the “All” filter (e.g., block2296 b is “NO”), then at block 2299 b, the subroutine process 2280 b,posts message: “Check filter settings” upon the display 116, 146.

FIG. 8 shows a basal adjustment process 2300 where block 2302 receivesthe BGBreakfast from Entry Point H (FIG. 7D) and the BGmidsleep (or fromEntry Point G (FIG. 7A). In some implementations, process 2300determines whether or not the BGBreakfast is less than BGmidsleep. Thebasal adjustment process 2300, at block 2304, selects the BGbreakfast asthe governing blood glucose BGgov for a basal adjustment when BGbreakfast is not less than BGmidsleep, and block 2306 selects theBGmidsleep as the governing blood glucose BGgov for the basal adjustmentwhen BG breakfast is less than BGmidsleep. The basal adjustment process2300 applies an adjustment factor (AF) function (FIG. 4) at block 2308using the BGgov selected from one of blocks 2304 or 2306. Specifically,the basal adjustment process 2300 determines the adjustment factor AF atblock 2308 as a function of the governing blood glucose BGgov. Inscenarios when there are an insufficient number of BG measurements forthe Midsleep BG time-bucket, i.e., when block 2204 (FIG. 7A) ofaggregation processes 2200 a is “YES”, the basal adjustment process2300, sets, at block 2328, the Adjustment Factor AF equal to 1. Thebasal adjustment process 2300 receives, at block 2328, the indication ofinsufficient BG data, i.e., preventing adjustment of the governing dose,from processes 2200 a via Entry Point S. At block 2310, the basaladjustment process 2300 determines the adjustment to the patient'sinsulin dose by the following equation:RecomBasal=(previous RecomBasal)*AF  (12)wherein the previous RecomBasal is provided from block 2312. The basaladjustment process 2300 transmits, at block 2310, the next recommendedbasal adjustment RecomBasal to the web-based application 198 of themanufacturer of the glucometer 124 or mobile device 110 b via EntryPoint Q of the SubQ outpatient process 1800 (FIG. 5A or FIG. 5B). Insome implementations, the basal adjustment process 2300 uses the dataflow process 1900 a (FIG. 6A) to transmit the next recommended basaladjustment RecomBasal directly to the mobile device 110 b via EntryPoint W (FIG. 6A). In other implementations, the basal adjustmentprocess 2300 uses the data flow process 1900 a (FIG. 6A) to transmit thenext recommended basal adjustment RecomBasal to the web-basedapplication 198 of the mobile device 110 b or the glucometer 124 viaEntry Point Q (FIG. 6A). Additionally, the basal adjustment process 2300provides, at block 2330, the RecomBasal to the merged database 1906(FIG. 6A) within the non-transitory memory 24, 134, 144.

Referring to FIG. 9, a meal bolus adjustment (withoutcarbohydrate-counting) process 2400 shows blocks 2402, 2404, 2406calculating next recommended meal boluses for scheduled meal boluses ofbreakfast, lunch, and dinner, respectively. The next recommended mealbolus for each scheduled meal bolus is based on the blood glucose BGmeasurement that occurs after the meal bolus being adjusted.

For calculating the next recommended breakfast bolus (block 2402), themeal bolus adjustment process 2400 receives, at block 2410, the BGmeasurement (e.g., BGlunch) that occurs after the breakfast meal bolusvia Entry Point H of the aggregation process 2200 b (FIG. 7D), and setsthe BGlunch as a governing blood glucose BGgov. The meal bolusadjustment process 2400 applies an adjustment factor (AF) function (FIG.4) at block 2412 using BGlunch as the BGgov. Specifically, the mealbolus adjustment process 2400 determines the adjustment factor AF atblock 2412 as function of the governing blood glucose BGgov (e.g.,BGlunch). In scenarios when there are an insufficient number of BGmeasurements for the Lunch BG time-bucket, i.e., when block 2234 (FIG.7D) of aggregation processes 2200 b is “YES”, the meal adjustmentprocess 2400, sets, at block 2440, the Adjustment Factor AF equal to 1.The meal bolus adjustment process 2400 receives, at block 2440, theindication of insufficient BG data, i.e., preventing adjustment of thegoverning dose, from the aggregation process 2200 b via Entry Point S.At block 2402, the meal bolus adjustment process 2400 determines theadjustment to the patient's breakfast meal bolus by the followingequation:RecomBreakBol=(previous RecomBreakBol)*AF  (15A)wherein the previous RecomBreakBol is provided from block 2408. Block2408 may obtain the previous RecomBreakBol from block 2442 associatedwith the merged database 1906 (FIG. 6A) within the non-transitory memory24, 134, 144. Thereafter, the meal bolus adjustment process 2400 usesthe data flow process 1900 a (FIG. 6A) to transmit the next recommendedbreakfast bolus to the web-based application 198 of the mobile device110 b or the glucometer 124 via Entry Point Q (FIG. 6A), or directly tothe mobile device 110 b via Entry Point W (FIG. 6A).

For calculating the next recommended lunch bolus (block 2404), the mealbolus adjustment process 2400 receives, at block 2416, the BGmeasurement (e.g., BGdinner) that occurs after the lunch meal bolus viaEntry Point H of the aggregation process 2200 b (FIG. 7D), and sets theBGdinner as a governing blood glucose BGgov. The meal bolus adjustmentprocess 2400 applies an adjustment factor (AF) function (FIG. 4) atblock 2418 using BGdinner as the BGgov. Specifically, the meal bolusadjustment process 2400 determines the adjustment factor AF at block2418 as a function of the governing blood glucose BGgov (e.g.,BGdinner). In scenarios when there are an insufficient number of BGmeasurements for the Dinner BG time-bucket, i.e., when block 2234 (FIG.7D) of aggregation processes 2200 b is “YES”, the meal adjustmentprocess 2400, sets, at block 2440, the Adjustment Factor AF equal to 1.The meal bolus adjustment process 2400 receives, at block 2440, theindication of insufficient BG data, i.e., preventing adjustment of thegoverning dose, from the aggregation process 2200 b via Entry Point S.At block 2404, the meal bolus adjustment process 2400 determines theadjustment to the patient's lunch meal bolus by the following equation:RecomLunchBol=(previous RecomLunchBol)*AF  (15B)wherein the previous RecomLunchBol is provided from block 2414. Block2414 may obtain the previous RecomLunchBol from block 2442 associatedwith the merged database 1906 (FIG. 6A) within the non-transitory memory24, 134, 144. Thereafter, the meal bolus adjustment process 2400 usesthe data flow process 1900 a (FIG. 6A) to transmit the next recommendedlunch bolus to the web-based application 198 of the mobile device 110 bor the glucometer 124 via Entry Point Q (FIG. 6A), or directly to themobile device 110 b via Entry Point W (FIG. 6A).

For calculating the next recommended dinner bolus (block 2406), the mealbolus adjustment process 2400 receives, at block 2422, the blood glucose(BG) measurement (e.g., BGbedtime) that occurs after the dinner mealbolus via Entry Point G of the non-meal aggregation process 2200 a (FIG.7A), and sets BGbedtime as a governing blood glucose BGgov. The mealbolus adjustment process 2400 applies an adjustment factor (AF) function(FIG. 4) at block 2424 using BGbedtime as the BGgov. Specifically, themeal bolus adjustment process 2400 determines the adjustment factor AFat block 2424 as a function of the governing blood glucose BGgov (e.g.,BGbedtime). In scenarios when there are an insufficient number of BGmeasurements for the Bedtime BG time-bucket, i.e., when block 2204 (FIG.7A) of aggregation process 2200 a is “YES”, the meal bolus adjustmentprocess 2400, sets, at block 2440, the Adjustment Factor AF equal to 1.The meal bolus adjustment process 2400 receives, at block 2440, theindication of insufficient BG data, i.e., preventing adjustment of thegoverning dose, from the aggregation process 2200 a via Entry Point S.At block 2406, the meal bolus adjustment process 2400 determines theadjustment to the patient's next dinner meal bolus by the followingequation:RecomDinnerBol=(previous RecomDinnerBol)*AF,  (15C)wherein the previous RecomDinnerBol is provided from block 2420. Block2420 may obtain the previous RecomDinnerBol from block 2442 associatedwith the merged database 1906 (FIG. 6A) within the non-transitory memory24, 134, 144. Thereafter, the meal bolus adjustment process 2400 usesthe data flow process 1900 a (FIG. 6A) to transmit the next recommendeddinner bolus to the web-based application 198 of the mobile device 110 bor the glucometer 124 via Entry Point Q (FIG. 6A), or directly to themobile device 110 b via Entry Point W (FIG. 6A).

In some implementations, the adjusted meal boluses set forth above maybe calculated using the grams of carbohydrate consumed by the patient 10and the Carbohydrate-to-Insulin Ratio CIR where the RecommendedBreakfast, Lunch and Dinner Boluses may be calculated as follows:RecomLunchBolus=(Carbohydrate gms in Lunch)/CIR  (16A)RecomDinnerBol=(Carbohydrate gms in Dinner)/CIR  (16B)RecBreakfastBol=(Carbohydrate gms in Breakfast)/CIR  (16C)

Referring to FIG. 10, a carbohydrate-insulin-ratio (CIR) adjustmentprocess 2500 shows blocks 2502, 2504, 2506 calculating next recommendedCIRs for scheduled meal boluses of breakfast, lunch and dinner,respectively. The next recommended CIR for each scheduled meal bolus isbased on the blood glucose BG measurement that occurs after the mealbolus associated with the CIR being adjusted.

For calculating the next recommended breakfast CIR (block 2502), the CIRadjustment process 2500 receives, at block 2510, the BG measurement(e.g., BGlunch) that occurs after the breakfast meal bolus via EntryPoint H of the aggregation process 2200 b (FIG. 7D), and sets theBGlunch as a governing blood glucose BGgov. The CIR adjustment process2500 applies an adjustment factor (AF) function (FIG. 4) at block 2512using BGlunch as the BGgov. Specifically, CIR adjustment process 2500determines the adjustment factor AF at block 2512 as a function of thegoverning blood glucose BGgov (e.g., BGlunch). In scenarios when thereare an insufficient number of BG measurements for the Lunch BGtime-bucket, i.e., when block 2234 (FIG. 7D) of aggregation processes2200 b is “YES”, the CIR adjustment process 2500, sets, at block 2540,the Adjustment Factor AF equal to 1. The CIR adjustment process 2500receives, at block 2540, the indication of insufficient BG data, i.e.,preventing adjustment of the governing dose, from the aggregationprocess 2200 b via Entry Point S. At block 2502, the CIR adjustmentprocess 2500 determines the adjustment to the patient's breakfast CIR bythe following equation:RecomBreakCIR=(previous RecomBreakCIR)/AF,  (17A)wherein the previous RecomBreakCIR is provided from block 2508. Block2508 may obtain the previous RecomBreakCIR from block 2542 associatedwith the merged database 1906 (FIG. 6A) within the non-transitory memory24, 134, 144. Thereafter, the CIR adjustment process 2500 uses the dataflow process 1900 a (FIG. 6A) to transmit the next recommended breakfastCIR to the web-based application 198 of the mobile device 110 b or theglucometer 124 via Entry Point Q (FIG. 6A), or directly to the mobiledevice 110 b via Entry Point W (FIG. 6A).

For calculating the next recommended lunch CIR (block 2504), the CIRadjustment process 2500 receives, at block 2516, the BG measurement(e.g., BGdinner) that occurs after the lunch meal bolus via Entry PointH of the aggregation process 2200 b (FIG. 7D), and sets the BGdinner asa governing blood glucose BGgov. The CIR adjustment process 2500 appliesan adjustment factor (AF) function (FIG. 4) at block 2518 using BGdinneras the BGgov. Specifically, the CIR adjustment process 2500 determinesthe adjustment factor AF at block 2518 as a function of the governingblood glucose BGgov (e.g., BGdinner). In scenarios when there are aninsufficient number of BG measurements for the Dinner BG time-bucket,i.e., when block 2234 (FIG. 7D) of aggregation processes 2200 b is“YES”, the CIR adjustment process 2500, sets, at block 2540, theAdjustment Factor AF equal to 1. The CIR adjustment process 2500receives, at block 2540, the indication of insufficient BG data, i.e.,preventing adjustment of the governing dose, from the aggregationprocess 2200 b via Entry Point S. At block 2504, the CIR adjustmentprocess 2500 determines the adjustment to the patient's lunch CIR by thefollowing equation:RecomLunchCIR=(previous RecomLunchCIR)/AF,  (17B)wherein the previous RecomLunchCIR is provided from block 2514. Block2514 may obtain the previous RecomLunchCIR from block 2542 associatedwith the merged database 1906 (FIG. 6A) within the non-transitory memory24, 134, 144. Thereafter, the CIR adjustment process 2500 uses the dataflow process 1900 a (FIG. 6A) to transmit the next recommendedbreakfastCIR to the web-based application 198 of the mobile device 110 bor the glucometer 124 via Entry Point Q (FIG. 6A), or directly to themobile device 110 b via Entry Point W (FIG. 6A).

For calculating the next recommended CIR dinner bolus (block 2506), theCIR adjustment process 2500 receives, at block 2522, the blood glucose(BG) measurement (e.g., BGbedtime) that occurs after the dinner mealbolus via Entry Point G of the non-meal aggregation process 2200 a (FIG.7A), and sets BGbedtime as a governing blood glucose BGgov. The CIRadjustment process 2500 applies an adjustment factor (AF) function (FIG.4) at block 2524 using BGbedtime as the BGgov. Specifically, the CIRadjustment process 2500 determines the adjustment factor AF at block2524 as a function of the governing blood glucose BGgov (e.g.,BGbedtime). In scenarios when there are an insufficient number of BGmeasurements for the Bedtime BG time-bucket, i.e., when block 2204 (FIG.7A) of aggregation process 2200 a is “YES”, the CIR adjustment process2500, sets, at block 2540, the Adjustment Factor AF equal to 1. The CIRadjustment process 2500 receives, at block 2540, the indication ofinsufficient BG data, i.e., preventing adjustment of the governing dose,from the aggregation process 2200 a via Entry Point S. At block 2506,the CIR adjustment process 2500 determines the adjustment to thepatient's next dinner CIR by the following equation:RecomDinnerCIR=(previous RecomDinnerCIR)/AF  (17C)wherein the previous RecomDinnerCIR is provided from block 2520. Block2520 may obtain the previous RecomDinnerCIR from block 2542 associatedwith the merged database 1906 (FIG. 6A) within the non-transitory memory24, 134, 144. Thereafter, the CIR adjustment process 2500 uses the dataflow process 1900 a (FIG. 6A) to transmit the next recommended dinnerCIR to the web-based application 198 of the mobile device 110 b or theglucometer 124 via Entry Point Q (FIG. 6A), or directly to the mobiledevice 110 b via Entry Point W (FIG. 6A).

FIG. 11 is a schematic view of exemplary components of the system ofFIGS. 1A-1C. FIG. 11 may be described with reference to the SubQoutpatient process 1800 b of FIG. 5B. In some implementations, theinsulin administration device 123 associated with the patient 10includes a smart pump 123 a or a smart pen 123 b that is capable ofcommunicating (e.g., syncing) with a patient device 110 such as a smartphone 110 b. In the example shown, the smart pen 123 b communicates withthe smart phone 110 b via Bluetooth, however, other wireless or wiredcommunications are possible. Likewise, in some implementations, theglucometer 124 associated with the patient 10 is capable ofcommunicating blood glucose measurements to the smart phone 110 b. Theglucometer 124 and smart phone 110 b may communicate via Bluetooth,infrared, cable, or any other communications. In some examples, theglucometer 124 communicates with a data translator 125, and the datatranslator 125 provides the blood glucose measurements from theglucometer 124 to the smart phone 110 b. The computing device 112 b ofthe smart phone 110 b may execute a mobile application 1198 forcommunicating with the dosing controller 160 such that information canbe communicated over the network 20 between the dosing controller 160and each of the smart pen 123 b and the glucometer 124. For example,dosing parameters adjusted by the dosing controller 160 may betransmitted to the smart phone 110 b and stored within memory 114 b. Thedosing parameters may include, but are not limited to: TargetBG,Correction Factor (CF), CIR for all day, CIR's for each meal,Recommended Breakfast Bolus, Recommended Lunch Bolus, Recommended DinnerBolus, Recommended Basal doses, number of Basal doses per day, and Basaldose scheduled times. As described above with reference to the data flowprocess 1900 a-c of FIGS. 6A-6C, the dosing parameters may be adjustedautomatically or manually initiated by the user 40 or patient 10.

In some implementations, upon the glucometer 124 determining a bloodglucose measurement, the glucometer 124 transmits the blood glucosemeasurement to the smart phone 110 b. The smart phone 110 b may renderthe blood glucose measurement upon the display 116 b and permit thepatient 10 to select the BGtype associated with the blood glucosemeasurement (e.g., blocks 1804 and 1806 of FIG. 5B). The smart phone 110b may transmit the BG measurement and the BG type to the dosingcontroller 160 via the network 20. In some implementations, the mobileapplication 1198 executing on the smart phone 110 b calculates acorrection bolus (CB) using EQ. 2 based upon the current correctionfactor (CF) and Target BG stored within the memory 114 b. In otherimplementations, the correction bolus (CB) is calculated using EQ. 10(block 714 of FIG. 3) by deducting from previously administered doses ofinsulin that are still active. The CF and Target BG may be provided whena previous dosing parameter adjustment was transmitted to the smartphone 110 b from the dosing controller 160.

In some implementations, recommended meal boluses may be determined bythe dosing controller 160 and sent to the smart phone 110 b during eachadjustment transmission and stored within the memory 114 b. For example,upon the patient 10 selecting the BG type for a given blood glucosemeasurement, the mobile application 1198 executing on the smartphone maydetermine the meal bolus (e.g., breakfast, lunch, or dinner) based uponthe BG type without using carb counting for the current meal. In someconfigurations, the mobile application 1198 executing on the smart phone110 b executes all functionality of the dosing controller 160, therebyeliminating the need for communications over the network. In someexamples, when the BG measurement requires the correction bolus, themobile application 1198 calculates a total bolus (e.g., mealbolus+correction bolus) and transmits the total bolus to the smart pen123 b. In some examples, the smart pen 123 b (using the administrationcomputing device 112 e, automatically dials in the total bolus for thedoser 223 b to administer. In some examples, the smart pen 123 breceives a recommended total bolus dose from the smart phone 110 btransmitted from the computing device 142 of the dosing controller 160via the network 20. In some examples, upon administration of an insulindose by the smart pen 123 b, the smart pen 123 b transmits the value ofthe administered dose to the smart phone 110 b for storage within memory114 a along with the associated BG measurement.

In some examples, the patient 10 may enter a number of carbohydrates fora current meal into the glucometer 124 for transmission to the smartphone 110 b or directly into the smart phone 110 b when a blood glucosemeasurement is received. Using a carbohydrate-to-insulin ratio (CIR)transmitted from the dosing controller 160 to the smart phone 110 b, themobile application 1198 executing on the smart phone may calculate therecommended meal bolus (e.g., breakfast, lunch or dinner) using one ofthe EQ. 16A-16C. In some examples, the CIR and CF are adjusted each timea BG measurement is received at the dosing controller 160 from theglucometer 124 using the smart phone 110 b to facilitate thetransmission thru the network 20. In other examples, the CIR and CF areadjusted when all the dosing parameters are adjusted (e.g., via thebatch download process) and transmitted to the smart phone 110 b forstorage within the memory 114 b.

FIG. 12A shows the display 146 of the health care provider computingsystem 140 displaying blood glucose data. A plot 502 depicts a modal dayscatter chart of blood glucose measurements over a period of time alongthe x-axis and blood glucose value along the y-axis. In the exampleshown, a target blood glucose range is depicted in the plot.Computational Information 504 depicts an average for patients' A1C value(6.8%), an average fasting blood glucose value (e.g., 138 mg/dl), anaverage BGs per day, a percent of BGs Within the target, a total numberof patients using basal bolus therapy, a total number of patients usingbasal/correction therapy, a total number of patients using a pump, and atotal number of patients using inhalants. Bar graph 506 depicts adistribution of blood glucose measurements in the target range and piechart 508 depicts a percentage of patients experiencing varying degreesof hypoglycemia.

FIG. 13 is a schematic view of an exemplary Carbohydrate-Insulin-Ratio(CIR) Adjustment in a Meal-by-Meal process 2600. There is a singlevariable for CIR. Blocks 2604, 2608, 2610, 2614, 2616 determine whetheror not a given meal type is associated with a BGtype for Breakfast,Lunch, Dinner, Bedtime, or MidSleep/Miscellaneous, respectively. For agiven meal, e.g. Lunch, the process obtains the CIR, at block 2628 fromthe previous meal calculations e.g. Breakfast, associated with block2624 (a few hours previous). The current BG is identified as the LunchBG at block 2608. The Lunch BG may be only seconds old. The Lunch BG issent to block 2618 as a governing blood glucose value BGgov fordetermining an Adjustment Factor AF using the Adjustment FactorFunction. Accordingly, at block 2628, the process 2600 calculates theCIR for Lunch by dividing the previous CIR for Breakfast by the AFdetermined at block 2618. Block 2628 provides the CIR for Lunch to block2640 for calculating the recommended lunch bolus by dividing anestimated number of carbohydrates to be consumed by the patient by theCIR for lunch. For calculating the CIR for Dinner, block 2632 may usethe CIR for Lunch calculated at block 2628. Process 2600 repeats,meal-by-meal, with the exception of the logic flow between Bedtime andBreakfast, whereat the Bedtime BG is ideally placed after Dinner togovern an adjustment to the current CIR. Therefore, the Bedtime BG atblock 2614 is the governing BG fed to the AF function at block 2622, andthe resulting AF is sent to block 2634. Also the current CIR arrives at2634 from the CIR for Dinner calculated at block 2632. The calculationat block 2634 involves dividing the current CIR by the AF to obtain anewly adjusted value of the CIR. In some implementations, a Bedtimesnack is allowed, using this value of the CIR. This value of the CIR(governed by the Bedtime BG) is passed without further adjustment to theBreakfast calculations the next day. In some implementations, anadditional CIR adjustment may be governed by the MidSleep BG.

Referring to FIG. 14, a method 1400 of administering insulin using asubcutaneous (SubQ) outpatient process 1800 includes receiving 1402subcutaneous information 216 for a patient 10 at a computing device 112,132, 142. The method 1400 executes 1404 the SubQ outpatient process1800. The method 1400 includes obtaining 1406 blood glucose data of thepatient 124 from a glucometer 124 in communication with the computingdevice 112, 132, 142. The blood glucose data includes blood glucosemeasurements of the patient 10 and/or doses of insulin administered bythe patient 10 associated with each blood glucose measurement. Themethod 1400 includes the computing device 112, 132, 142 determining 1408a next recommended insulin dosage for the patient 10 based on theobtained blood glucose data and the subcutaneous information 216 a. Themethod further includes 1400 the computing device 112, 132, 142transmitting the next recommended insulin dosage to a portable deviceassociated with the patient 10. The portable device 110 a-e displays thenext recommended insulin dose.

Various implementations of the systems and techniques described here canbe realized in digital electronic circuitry, integrated circuitry,specially designed ASICs (application specific integrated circuits),computer hardware, firmware, software, and/or combinations thereof.These various implementations can include implementation in one or morecomputer programs that are executable and/or interpretable on aprogrammable system including at least one programmable processor, whichmay be special or general purpose, coupled to receive data andinstructions from, and to transmit data and instructions to, a storagesystem, at least one input device, and at least one output device.

These computer programs (also known as programs, software, softwareapplications or code) include machine instructions for a programmableprocessor and can be implemented in a high-level procedural and/orobject-oriented programming language, and/or in assembly/machinelanguage. As used herein, the terms “machine-readable medium” and“computer-readable medium” refer to any computer program product,apparatus and/or device (e.g., magnetic discs, optical disks, memory,Programmable Logic Devices (PLDs)) used to provide machine instructionsand/or data to a programmable processor, including a machine-readablemedium that receives machine instructions as a machine-readable signal.The term “machine-readable signal” refers to any signal used to providemachine instructions and/or data to a programmable processor.

Implementations of the subject matter and the functional operationsdescribed in this specification can be implemented in digital electroniccircuitry, or in computer software, firmware, or hardware, including thestructures disclosed in this specification and their structuralequivalents, or in combinations of one or more of them. Moreover,subject matter described in this specification can be implemented as oneor more computer program products, i.e., one or more modules of computerprogram instructions encoded on a computer readable medium for executionby, or to control the operation of, data processing apparatus. Thecomputer readable medium can be a machine-readable storage device, amachine-readable storage substrate, a memory device, a composition ofmatter affecting a machine-readable propagated signal, or a combinationof one or more of them. The terms “data processing apparatus”,“computing device” and “computing processor” encompass all apparatus,devices, and machines for processing data, including by way of example aprogrammable processor, a computer, or multiple processors or computers.The apparatus can include, in addition to hardware, code that creates anexecution environment for the computer program in question, e.g., codethat constitutes processor firmware, a protocol stack, a databasemanagement system, an operating system, or a combination of one or moreof them. A propagated signal is an artificially generated signal, e.g.,a machine-generated electrical, optical, or electromagnetic signal thatis generated to encode information for transmission to suitable receiverapparatus.

A computer program (also known as an application, program, software,software application, script, or code) can be written in any form ofprogramming language, including compiled or interpreted languages, andit can be deployed in any form, including as a stand-alone program or asa module, component, subroutine, or other unit suitable for use in acomputing environment. A computer program does not necessarilycorrespond to a file in a file system. A program can be stored in aportion of a file that holds other programs or data (e.g., one or morescripts stored in a markup language document), in a single filededicated to the program in question, or in multiple coordinated files(e.g., files that store one or more modules, sub programs, or portionsof code). A computer program can be deployed to be executed on onecomputer or on multiple computers that are located at one site ordistributed across multiple sites and interconnected by a communicationnetwork.

The processes and logic flows described in this specification can beperformed by one or more programmable processors executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application specific integrated circuit).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read only memory ora random access memory or both. The essential elements of a computer area processor for performing instructions and one or more memory devicesfor storing instructions and data. Generally, a computer will alsoinclude, or be operatively coupled to receive data from or transfer datato, or both, one or more mass storage devices for storing data, e.g.,magnetic, magneto optical disks, or optical disks. However, a computerneed not have such devices. Moreover, a computer can be embedded inanother device, e.g., a mobile telephone, a personal digital assistant(PDA), a mobile audio player, a Global Positioning System (GPS)receiver, to name just a few. Computer readable media suitable forstoring computer program instructions and data include all forms ofnon-volatile memory, media and memory devices, including by way ofexample semiconductor memory devices, e.g., EPROM, EEPROM, and flashmemory devices; magnetic disks, e.g., internal hard disks or removabledisks; magneto optical disks; and CD ROM and DVD-ROM disks. Theprocessor and the memory can be supplemented by, or incorporated in,special purpose logic circuitry.

To provide for interaction with a user, one or more aspects of thedisclosure can be implemented on a computer having a display device,e.g., a CRT (cathode ray tube), LCD (liquid crystal display) monitor, ortouch screen for displaying information to the user and optionally akeyboard and a pointing device, e.g., a mouse or a trackball, by whichthe user can provide input to the computer. Other kinds of devices canbe used to provide interaction with a user as well; for example,feedback provided to the user can be any form of sensory feedback, e.g.,visual feedback, auditory feedback, or tactile feedback; and input fromthe user can be received in any form, including acoustic, speech, ortactile input. In addition, a computer can interact with a user bysending documents to and receiving documents from a device that is usedby the user; for example, by sending web pages to a web browser on auser's client device in response to requests received from the webbrowser.

One or more aspects of the disclosure can be implemented in a computingsystem that includes a backend component, e.g., as a data server, orthat includes a middleware component, e.g., an application server, orthat includes a frontend component, e.g., a client computer having agraphical user interface or a Web browser through which a user caninteract with an implementation of the subject matter described in thisspecification, or any combination of one or more such backend,middleware, or frontend components. The components of the system can beinterconnected by any form or medium of digital data communication,e.g., a communication network. Examples of communication networksinclude a local area network (“LAN”) and a wide area network (“WAN”), aninter-network (e.g., the Internet), and peer-to-peer networks (e.g., adhoc peer-to-peer networks).

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. In someimplementations, a server transmits data (e.g., an HTML page) to aclient device (e.g., for purposes of displaying data to and receivinguser input from a user interacting with the client device). Datagenerated at the client device (e.g., a result of the user interaction)can be received from the client device at the server.

While this specification contains many specifics, these should not beconstrued as limitations on the scope of the disclosure or of what maybe claimed, but rather as descriptions of features specific toparticular implementations of the disclosure. Certain features that aredescribed in this specification in the context of separateimplementations can also be implemented in combination in a singleimplementation. Conversely, various features that are described in thecontext of a single implementation can also be implemented in multipleimplementations separately or in any suitable sub-combination. Moreover,although features may be described above as acting in certaincombinations and even initially claimed as such, one or more featuresfrom a claimed combination can in some cases be excised from thecombination, and the claimed combination may be directed to asub-combination or variation of a sub-combination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multi-tasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the embodiments described above should not be understoodas requiring such separation in all embodiments, and it should beunderstood that the described program components and systems cangenerally be integrated together in a single software product orpackaged into multiple software products.

A number of implementations have been described. Nevertheless, it willbe understood that various modifications may be made without departingfrom the spirit and scope of the disclosure. Accordingly, otherimplementations are within the scope of the following claims. Forexample, the actions recited in the claims can be performed in adifferent order and still achieve desirable results.

What is claimed is:
 1. A method comprising: receiving, at dataprocessing hardware, sequential scheduled blood glucose time intervalsthroughout a day from a healthcare provider computing device incommunication with the data processing hardware, each scheduled bloodglucose time interval associated with a corresponding adjustable timeboundary that does not overlap with other time boundaries associatedwith the other scheduled blood glucose time intervals; obtaining, at thedata processing hardware, blood glucose data of the patient, the bloodglucose data including blood glucose measurements of the patient andblood glucose times associated with the blood glucose measurements; foreach scheduled blood glucose time interval, aggregating, by the dataprocessing hardware, the blood glucose measurements associated with thecorresponding scheduled blood glucose time interval based on the bloodglucose times to determine a corresponding representative aggregateblood glucose measurement associated with the corresponding scheduledblood glucose time interval; determining, by the data processinghardware, a next recommended carbohydrate-to-insulin ratio for thepatient during a selected time interval based on the representativeaggregate blood glucose measurement associated with the scheduled bloodglucose time interval that occurs immediately after the selected timeinterval; and transmitting the next recommended carbohydrate-to-insulinratio for the patient during the selected time interval from the dataprocessing hardware to a patient device, the patient device executing amobile application configured to: receive a number of carbohydratesconsumed by the patient during the selected time interval; determine arecommended meal bolus for the patient based on the number ofcarbohydrates consumed by the patient and the next recommendedcarbohydrate-to-insulin ratio for the patient during the selected time;and transmit the recommended meal bolus to an administration device incommunication with the patient device, the administration deviceconfigured to automatically dial in a number of units of insulin for therecommended meal bolus and administer the number of units of insulin forthe recommended meal bolus.
 2. The method of claim 1, wherein the mobileapplication is further configured to display the number of carbohydratesconsumed by the patient during the selected time interval on a screen ofthe patient device.
 3. The method of claim 1, wherein the mobileapplication is further configured to display the recommended meal bolusfor the patient on the screen of the patient device.
 4. The method ofclaim 1, wherein obtaining the blood glucose data comprises receivingthe blood glucose data from a remote computing device in communicationwith the data processing hardware during a batch download process, theremote computing device executing a download program for downloading theblood glucose data from a glucometer or a continuous glucose monitoringsystem associated with the patient.
 5. The method of claim 1, whereinobtaining the blood glucose data comprises receiving the blood glucosedata from the patient device in communication with a glucometerassociated with the patient, the patient device receiving the bloodglucose data from the glucometer.
 6. The method of claim 1, whereinobtaining the blood glucose data comprises receiving the blood glucosedata from the patient device in communication with a continuous glucosemonitoring system, the patient device receiving the blood glucose datafrom the continuous glucose monitoring system.
 7. The method of claim 1,wherein the administration device comprises a smart insulin pen.
 8. Themethod of claim 1, wherein the administration device comprises aninsulin pump.
 9. The method of claim 1, further comprising: aggregating,by the data processing hardware, one or more of the blood glucosemeasurements associated with a breakfast blood glucose time interval todetermine a representative aggregate breakfast blood glucosemeasurement; aggregating, by the data processing hardware, one or moreof the blood glucose measurements associated with a midsleep bloodglucose time interval to determine a representative aggregate midsleepblood glucose measurement; selecting, by the data processing hardware, agoverning blood glucose as a lesser one of the representative aggregatemidsleep blood glucose measurement or the representative aggregatebreakfast blood glucose measurement; determining, by the data processinghardware, an adjustment factor for adjusting a next recommended basaldosage based on the selected governing blood glucose measurement;obtaining, at the data processing hardware, a previous day recommendedbasal dosage; and determining, by the data processing hardware, the nextrecommended basal dosage by multiplying the adjustment factor times theprevious day recommended basal dosage.
 10. The method of claim 1,further comprising: aggregating, by the data processing hardware, one ormore of the blood glucose measurements associated with a lunch bloodglucose time interval to determine a representative aggregate lunchblood glucose measurement; selecting, by the data processing hardware, agoverning blood glucose as the representative aggregate lunch bloodglucose measurement; determining, by the data processing hardware, anadjustment factor for adjusting a next recommended breakfastcarbohydrate-to-insulin ratio based on the selected governing bloodglucose measurement; obtaining, at the data processing hardware, aprevious day recommended breakfast carbohydrate-to-insulin ratio; anddetermining, by the data processing hardware, the next recommendedbreakfast carbohydrate-to-insulin ratio by dividing the previous dayrecommended breakfast carbohydrate-to-insulin ratio by the adjustmentfactor.
 11. The method of claim 1, further comprising: aggregating, bythe data processing hardware, one or more of the blood glucosemeasurements associated with a dinner blood glucose time interval todetermine a representative aggregate dinner blood glucose measurement;selecting, by the data processing hardware, a governing blood glucose asthe representative aggregate dinner blood glucose measurement;determining, by the data processing hardware, an adjustment factor foradjusting a next recommended lunch carbohydrate-to-insulin ratio basedon the selected governing blood glucose measurement; obtaining, at thedata processing hardware, a previous day recommended lunchcarbohydrate-to-insulin ratio; and determining, by the data processinghardware, the next recommended lunch carbohydrate-to-insulin ratio bydividing the previous day recommended lunch carbohydrate-to-insulinratio by the adjustment factor.
 12. The method of claim 1, furthercomprising: aggregating, by the data processing hardware, one or more ofthe blood glucose measurements associated with a bedtime blood glucosetime interval to determine a representative aggregate bedtime bloodglucose measurement; selecting, by the data processing hardware, agoverning blood glucose as the representative aggregate bedtime bloodglucose measurement; determining, by the data processing hardware, anadjustment factor for adjusting a next recommended dinnercarbohydrate-to-insulin ratio based on the selected governing bloodglucose measurement; obtaining, at the data processing hardware, aprevious day recommended dinner carbohydrate-to-insulin ratio; anddetermining, by the data processing hardware, the next recommendeddinner carbohydrate-to-insulin ratio by dividing the previous dayrecommended dinner carbohydrate-to-insulin ratio by the adjustmentfactor.
 13. The method of claim 1, further comprising: aggregating, bythe data processing hardware, one or more of the blood glucosemeasurements associated with a selected one of the scheduled bloodglucose time intervals to determine a representative aggregate bloodglucose measurement associated with the selected scheduled blood glucosetime interval; selecting, by the data processing hardware, a governingblood glucose as the representative aggregate blood glucose measurementassociated with the selected scheduled blood glucose time interval;determining, by the data processing hardware, an adjustment factor foradjusting a next recommended meal bolus governed by the selectedscheduled blood glucose time interval based on the selected governingblood glucose measurement; obtaining, at the data processing hardware, aprevious day recommended meal bolus governed by the selected scheduledblood glucose time interval; and determining, by the data processinghardware, the next recommended meal bolus by multiplying the adjustmentfactor times the previous day recommended meal bolus.
 14. The method ofclaim 1, further comprising: receiving, at the data processing hardware,a specified date range from the remote healthcare provider computingdevice; and aggregating, using the data processing hardware, one or moreof the blood glucose measurements associated with at least one of thescheduled blood glucose time intervals and within the specified daterange.
 15. The method of claim 1, wherein the representative aggregateblood glucose measurement includes one of: a mean blood glucose valuefor the associated scheduled blood glucose time interval; or a medianblood glucose value for the associated scheduled blood glucose timeinterval.
 16. A dosing controller comprising: data processing hardware;and memory hardware in communication with the data processing hardware,the memory hardware storing instructions for a subcutaneous outpatientprogram that when executed on the data processing hardware cause thedata processing hardware to perform operations comprising: receivingsequential scheduled blood glucose time intervals throughout a day froma healthcare provider computing device in communication with the dataprocessing hardware, each scheduled blood glucose time intervalassociated with a corresponding adjustable time boundary that does notoverlap with other time boundaries associated with the other scheduledblood glucose time intervals; obtaining blood glucose data of thepatient, the blood glucose data including blood glucose measurements ofthe patient and blood glucose times associated with the blood glucosemeasurements; for each scheduled blood glucose time interval,aggregating the blood glucose measurements associated with thecorresponding scheduled blood glucose time interval based on the bloodglucose times to determine a corresponding representative aggregateblood glucose measurement associated with the corresponding scheduledblood glucose time interval; determining a next recommendedcarbohydrate-to-insulin ratio for the patient during a selected timeinterval based on the representative aggregate blood glucose measurementassociated with the scheduled blood glucose time interval that occursimmediately after the selected time interval; and transmitting the nextrecommended carbohydrate-to-insulin ratio for the patient during theselected time interval to a patient device, the patient device executinga mobile application configured to: receive a number of carbohydratesconsumed by the patient during the selected time interval; determine arecommended meal bolus for the patient based on the number ofcarbohydrates consumed by the patient and the next recommendedcarbohydrate-to-insulin ratio for the patient during the selected time;and transmit the recommended meal bolus to an administration device incommunication with the patient device, the administration deviceconfigured to automatically dial in a number of units of insulin for therecommended meal bolus and administer the number of units of insulin forthe recommended meal bolus.
 17. The dosing controller of claim 16,wherein the mobile application is further configured to display thenumber of carbohydrates consumed by the patient during the selected timeinterval on a screen of the patient device.
 18. The dosing controller ofclaim 16, wherein the mobile application is further configured todisplay the recommended meal bolus for the patient on the screen of thepatient device.
 19. The dosing controller of claim 16, wherein obtainingthe blood glucose data comprises receiving the blood glucose data from aremote computing device in communication with the data processinghardware during a batch download process, the remote computing deviceexecuting a download program for downloading the blood glucose data froma glucometer or a continuous glucose monitoring system associated withthe patient.
 20. The dosing controller of claim 16, wherein obtainingthe blood glucose data comprises receiving the blood glucose data fromthe patient device in communication with a glucometer associated withthe patient, the patient device receiving the blood glucose data fromthe glucometer.
 21. The dosing controller of claim 16, wherein obtainingthe blood glucose data comprises receiving the blood glucose data fromthe patient device in communication with a continuous glucose monitoringsystem, the patient device receiving the blood glucose data from thecontinuous glucose monitoring system.
 22. The dosing controller of claim16, wherein the administration device comprises a smart insulin pen. 23.The dosing controller of claim 16, wherein the administration devicecomprises an insulin pump.
 24. The dosing controller of claim 16,wherein the operations further comprise: aggregating one or more of theblood glucose measurements associated with a breakfast blood glucosetime interval to determine a representative aggregate breakfast bloodglucose measurement; aggregating one or more of the blood glucosemeasurements associated with a midsleep blood glucose time interval todetermine a representative aggregate midsleep blood glucose measurement;selecting a governing blood glucose as a lesser one of therepresentative aggregate midsleep blood glucose measurement or therepresentative aggregate breakfast blood glucose measurement;determining an adjustment factor for adjusting a next recommended basaldosage based on the selected governing blood glucose measurement;obtaining a previous day recommended basal dosage; and determining thenext recommended basal dosage by multiplying the adjustment factor timesthe previous day recommended basal dosage.
 25. The dosing controller ofclaim 16, wherein the operations further comprise: aggregating one ormore of the blood glucose measurements associated with a lunch bloodglucose time interval to determine a representative aggregate lunchblood glucose measurement; selecting a governing blood glucose as therepresentative aggregate lunch blood glucose measurement; determining anadjustment factor for adjusting a next recommended breakfastcarbohydrate-to-insulin ratio based on the selected governing bloodglucose measurement; obtaining a previous day recommended breakfastcarbohydrate-to-insulin ratio; and determining the next recommendedbreakfast carbohydrate-to-insulin ratio by dividing the previous dayrecommended breakfast carbohydrate-to-insulin ratio by the adjustmentfactor.
 26. The dosing controller of claim 16, wherein the operationsfurther comprise: aggregating one or more of the blood glucosemeasurements associated with a dinner blood glucose time interval todetermine a representative aggregate dinner blood glucose measurement;selecting a governing blood glucose as the representative aggregatedinner blood glucose measurement; determining an adjustment factor foradjusting a next recommended lunch carbohydrate-to-insulin ratio basedon the selected governing blood glucose measurement; obtaining aprevious day recommended lunch carbohydrate-to-insulin ratio; anddetermining the next recommended lunch carbohydrate-to-insulin ratio bydividing the previous day recommended lunch carbohydrate-to-insulinratio by the adjustment factor.
 27. The dosing controller of claim 16,wherein the operations further comprise: aggregating one or more of theblood glucose measurements associated with a bedtime blood glucose timeinterval to determine a representative aggregate bedtime blood glucosemeasurement; selecting a governing blood glucose as the representativeaggregate bedtime blood glucose measurement; determining an adjustmentfactor for adjusting a next recommended dinner carbohydrate-to-insulinratio based on the selected governing blood glucose measurement;obtaining a previous day recommended dinner carbohydrate-to-insulinratio; and determining the next recommended dinnercarbohydrate-to-insulin ratio by dividing the previous day recommendeddinner carbohydrate-to-insulin ratio by the adjustment factor.
 28. Thedosing controller of claim 16, wherein the operations further comprise:aggregating one or more of the blood glucose measurements associatedwith a selected one of the scheduled blood glucose time intervals todetermine a representative aggregate blood glucose measurementassociated with the selected scheduled blood glucose time interval;selecting a governing blood glucose as the representative aggregateblood glucose measurement associated with the selected scheduled bloodglucose time interval; determining an adjustment factor for adjusting anext recommended meal bolus governed by the selected scheduled bloodglucose time interval based on the selected governing blood glucosemeasurement; obtaining a previous day recommended meal bolus governed bythe selected scheduled blood glucose time interval; and determining thenext recommended meal bolus by multiplying the adjustment factor timesthe previous day recommended meal bolus.
 29. The dosing controller ofclaim 16, wherein the operations further comprise: receiving a specifieddate range from the remote healthcare provider computing device; andaggregating one or more of the blood glucose measurements associatedwith at least one of the scheduled blood glucose time intervals andwithin the specified date range.
 30. The dosing controller of claim 16,wherein the representative aggregate blood glucose measurement includesone of: a mean blood glucose value for the associated scheduled bloodglucose time interval; or a median blood glucose value for theassociated scheduled blood glucose time interval.